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Budapest University of Technology and Economics
Faculty of Transportation Engineering and Vehicle Engineering
Department of Transport Technology and Economics
Efficient application of road pricing schemes in the
era of autonomous and shared autonomous vehicles
The dissertation submitted by:
Mohamad Shatanawi
In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy
Supervisor:
Dr. Mészáros Ferenc
Budapest,
2022
Abstract
Rapid worldwide research and development related to autonomous and shared
autonomous vehicles (AVs and SAVs) and their expected presence on roads capture the
attention of the public, decision-makers, industry, and academics. AVs and SAVs are expected
to dominate automotive markets in the future due to their distinctive benefits: increased road
safety, better utilization of travel time, improved energy consumption, enhanced traffic
throughput, and expected environmental benefits are examples of some of the positive
implications of these vehicles. However, AVs and SAVs will most likely increase the traveled
miles and number of trips on roads because of their greater accessibility, which will most likely
aggravate congestion. Therefore, there is a foreseen need for traffic regulation policies like road
pricing (RP) to alleviate congestion-related problems in the era of AVs and SAVs.
On the one hand, AVs and SAVs possess advanced technology that allows for the
application of advanced RP schemes that is anticipated to be implemented in the presence of
driverless vehicles. On the other hand, RP has been proven effective in reducing traffic-related
problems, for example, pollution in Milan and congestion in Stockholm. Despite this, the public
acceptance of such a policy is considered low, which is a major reason for the scheme's failure.
Therefore, this dissertation investigates the possible approaches to applying RP successfully
and efficiently in the era of AVs and SAVs.
For a successful implementation of RP, the key requirement is public acceptability,
which I investigated through a two-step approach: (1) I distributed a survey based on well-
known methodologies in five capitals to define the factors that affect RP acceptability, (2) I
developed the previous methodologies and disseminated a survey in four countries to
investigate the factors that may influence RP acceptability in the era of driverless vehicles and
driverless vehicle adoption in the presence of RP. I utilized different econometric models in
analyzing the collected data to provide insight into the public perception of RP, AVs, and SAVs.
For instance, a factor analysis was applied to minimize the large set of items into a lower
number of factors. A multinomial logit model was generated to obtain the utility function
parameters of conventional cars, AVs, and SAVs. In addition, multiple linear regression was
applied to investigate RP acceptability as a function of all examined factors.
The results show that, in line with previous research, people who enjoy driving are less
likely to choose AVs and SAVs, whereas environmentally oriented users are more likely to opt
for AVs and SAVs. On the other hand, my research confirms the importance of other factors,
such as the positive impact of the willingness to share personal trips with other passengers on
RP acceptability and AV and SAV choice. Furthermore, the results demonstrate the
interdependency between the factors influencing RP acceptability and AV and SAV choice. To
the best of my knowledge, this study is the first to RP acceptability and AV and SAV adoption
while also examining the impacts of various factors on both. Moreover, the results indicate that
the identity of each case study and its general policy implications determine which factors
significantly affect the public acceptability of the RP scheme.
For an efficient application of RP, I utilized dynamic traffic assignment using a transport
network model for Budapest within the traffic macroscopic simulation software "Visum"
through a two-step approach to investigate: (1) the impact of the emergence of AVs and SAVs
on the Budapest network and consumer surplus in alternative future scenarios (2) the impact of
three RP strategies (static and dynamic) on network performance and social welfare in the same
alternative future scenarios.
Three future scenarios for the years 2030 and 2050 are presented and characterized by
different penetration rates of AVs and SAVs to reflect the uncertainty in the market share of
future cars. Moreover, the travel demand of the developed scenarios was obtained from The
Centre for Budapest Transport projections for the respective years, where the total predicted
private transport demand was 2.23, and 2.31 million trips per day for the years 2030, and 2050,
respectively. In the "Mix-Traffic" scenario for 2030, conventional cars, AVs, and SAVs operate
together in the network. The other two scenarios comprise only AVs and SAVs and are assumed
for the year 2050, where the "AV-Focused" scenario represents high dependency on privately
owned AV, and the "SAV-Focused" scenario reflects a high usage of SAV fleets. I also
compared the implications of three distinct RP strategies in Budapest's proposed future traffic
scenarios. The pricing schemes consisted of a static-fixed toll (bridge toll scheme), a static-
variable toll (distance-based scheme), and a dynamic RP (link-based scheme).
The results regarding the impact of the deployment of AV and SAV on Budapest's
network reveal that: from a traffic perspective, the emergence of AVs and SAVs would improve
the overall network performance; furthermore, better performance was observed with
increasing the share distribution of SAVs, where the lowest queues length, minimum delays,
maximum velocity, and lowest vehicle kilometers traveled took place in the SAV-Focused
scenario, followed by AV-Focused and Mix-Traffic scenarios, respectively. Similarly, the
consumer surplus increased in all future scenarios, where the highest increment occurred in the
AV-Focused scenario. Consequently, the advent of AVs and SAVs will improve traffic
performance and increase consumer surplus, benefiting road users and authorities. The results
regarding the implications of the applied pricing strategies demonstrate that the impact of RP
schemes differs according to the change in penetration rates of AVs and SAVs. Nevertheless,
considering the gained social benefits, implementing a dynamic pricing strategy (Link-based
Scheme) in the case of AV-Focused and SAV-Focused scenarios performed better than static
ones. On the contrary, the static pricing strategies (i.e., Bridge Toll and Distance-based
Schemes) outperformed the dynamic ones in the Mix-Traffic scenario. Furthermore, the link-
based scheme generated the maximum revenues (i.e., gathered tolls).
Acknowledgment
I wish to express my profound gratitude to my supervisor Dr. Mészáros Ferenc, for his
kind support and for steering me in the right direction whenever I needed it. Dr. Mészáros
Ferenc has been a teacher, a friend, and a role model, providing me with invaluable guidance
and suggestions that enabled me to conduct my research successfully. Likewise, I want to
extend my deepest gratitude to my faculty teachers, members, colleagues, and admins;
especially Prof. Dr. Tánczos Katalin, Prof. Dr. Török Ádám, Dr. Sipos Tibor, Dr. Hörcher
Dániel, Dr. Berki Zsolt, Dr. Duleba Szabolcs, Dr. Abdelkhalek Fatma, Szabó Zsombor, and
Alatawneh Anas for their critical review, advice, and invaluable assistance during the research
time, and to the entire Faculty of Transportation and Vehicle Engineering team for creating a
supportive environment for my personal and professional development.
A special note of appreciation is due to everyone who participated in filling the
distributed surveys and to my colleagues who supported me with surveys translation into the
local languages and helped to distribute them worldwide. I also want to register my appreciation
to the PTV Group and the Budapest Transport Centre (BKK) for generously providing their full
technical support to bring the best out of this research.
My special thanks to the Tempus Public Foundation for granting me the scholarship and
the Ministry of Public Works and Housing of Jordan for supporting me through the complete
Ph.D. program. All these institutes have made it possible to conduct this research and achieve
the results.
Dedication
This work is dedicated to
The memory of my father, Mahmoud, who paved the way for me in his words and actions
Father, you will never be forgotten
To my kind mother, Asma, who always lifted me up and encouraged me
To my brothers: Nabeel, Ahmad, Bashar, Aqeel, and Taha
To my beloved sister, Haneen, flower of my life
To my sweet nieces Maria & Jawana
Table of Contents
Abstract ..................................................................................................................................... 2
Acknowledgment ...................................................................................................................... 4
Dedication ................................................................................................................................. 5
Table of Contents ..................................................................................................................... 6
List of Tables ............................................................................................................................. 9
List of Figures ......................................................................................................................... 10
List of Abbreviations .............................................................................................................. 11
Chapter One - Introduction .................................................................................................. 13
1.1 Background ............................................................................................................. 13
1.2 Motivation and Research Novelty ......................................................................... 14
1.3 Research Questions ................................................................................................ 16
1.4 Used Scientific Methods ......................................................................................... 17
1.5 Dissertation Outline ............................................................................................... 19
2 Chapter Two - Urban Road Pricing Acceptability: An International Comparative
Study ........................................................................................................................................ 20
2.1 Introduction ............................................................................................................ 20
2.2 Literature Review ................................................................................................... 21
2.3 Theoretical Background ........................................................................................ 23
2.4 In-depth Analyses ................................................................................................... 25
2.4.1 Sampling .......................................................................................................................................... 25
2.4.2 Quantitative Data Analysis Methods .............................................................................................. 25
2.4.3 The Proposed Strategy .................................................................................................................... 26
2.5 Results ..................................................................................................................... 27
2.5.1 Descriptive Statistics ....................................................................................................................... 27
2.5.2 Factor Analysis ................................................................................................................................ 27
2.5.3 Bivariate Analysis: Hypothesis Testing .......................................................................................... 28
2.5.4 Multivariate Analysis: Regression Analysis ................................................................................... 30
2.6 Discussion and Conclusions ................................................................................... 31
3 Chapter Three - The Interrelationship between Road Pricing Acceptability and
Self-Driving Vehicle Adoption: Insights from Four Countries .......................................... 34
3.1 Introduction ............................................................................................................ 34
3.2 Literature Review ................................................................................................... 36
3.2.1 Adoption of AVs ............................................................................................................................. 36
3.2.2 Adoption of SAVs ........................................................................................................................... 38
3.3 Theoretical Background ........................................................................................ 39
3.4 In-depth Analyses ................................................................................................... 40
3.4.1 Survey Design ................................................................................................................................. 40
3.4.2 Survey Instrument ........................................................................................................................... 41
3.4.3 Analytical Methods ......................................................................................................................... 42
3.4.4 Descriptive Statistics ....................................................................................................................... 43
3.5 Results ..................................................................................................................... 45
3.5.1 RP Acceptability ............................................................................................................................. 45
3.5.2 AV and SAV Adoption ................................................................................................................... 46
3.6 Discussion ................................................................................................................ 51
3.6.1 RP Acceptability ............................................................................................................................. 51
3.6.2 AV and SAV Adoption ................................................................................................................... 51
3.6.3 Result’s Summary ........................................................................................................................... 53
3.7 Conclusion ............................................................................................................... 54
3.7.1 Insights for Policy Implication ........................................................................................................ 55
3.7.2 Limitations and Directions for Future Research ............................................................................. 56
4 Chapter Four - Implications of the Emergence of Autonomous Vehicles and Shared
Autonomous Vehicles: A Budapest Perspective .................................................................. 58
4.1 Introduction ............................................................................................................ 58
4.2 Research Framework ............................................................................................. 59
4.2.1 EFM Macroscopic Model on PTV Visum ...................................................................................... 59
4.2.2 Simulation of AVs Using SBA ....................................................................................................... 61
4.2.2.1 Reaction Time under SBA ..................................................................................................... 62
4.2.2.2 SBA Parameters ..................................................................................................................... 63
4.2.2.3 Utility Function, VOTT, and Social Welfare ........................................................................ 64
4.2.3 Simulation of SAVs Using SBA ..................................................................................................... 66
4.2.3.1 Simulation Framework of the SAV System .......................................................................... 66
4.2.3.2 SAV Supply Modeling .......................................................................................................... 67
4.3 Future Traffic Simulation Scenarios .................................................................... 69
4.4 Results and Discussion ........................................................................................... 71
4.5 Conclusion ............................................................................................................... 77
5 Chapter Five - Implications of Static and Dynamic Road Pricing Strategies in the
Era of Autonomous and Shared Autonomous Vehicles Using Simulation-Based Dynamic
Traffic Assignment: The Case of Budapest ......................................................................... 80
5.1 Introduction ............................................................................................................ 80
5.2 Overview of the Research on RP for Self-driving Vehicles ................................ 81
5.3 Pricing Strategies .................................................................................................... 83
5.3.1 Static RP Strategies ......................................................................................................................... 83
5.3.2 Dynamic RP Strategy ...................................................................................................................... 85
5.4 Results ..................................................................................................................... 87
5.4.1 Changes in TPP ............................................................................................................................... 87
5.4.2 Generated Revenues, Consumer Surplus, and Social Welfare ....................................................... 93
5.5 Discussion and Policy Implications ....................................................................... 94
5.6 Conclusion ............................................................................................................... 95
6 Chapter Six - The Summary of Scientific Results and Future Research .................. 98
References ............................................................................................................................. 102
List of Publications (Own): .................................................................................................. 114
Appendix ............................................................................................................................... 115
List of Tables
Table 1. The Proposed Pricing Strategy ................................................................................... 26
Table 2. Factor Analysis: Outcome factors and their internal consistency .............................. 28
Table 3. Mean of pricing scheme acceptability factor scores by background characteristics for
cities ......................................................................................................................................... 28
Table 4. Multiple regression coefficients of the scheme pricing acceptability model for each
city ............................................................................................................................................ 30
Table 5. Overview of the key characteristics of the countries under study ............................. 36
Table 6. Distribution of Responses Across Countries .............................................................. 41
Table 7. Factor analysis example ............................................................................................. 42
Table 8. Descriptive values of some factors generated by factor analysis ............................... 42
Table 9. MLR Parameters of RP acceptability ......................................................................... 45
Table 10. MNL of vehicle adoption in Hungary ...................................................................... 47
Table 11. MNL of vehicle adoption in Jordan ......................................................................... 48
Table 12. MNL of vehicle adoption in Ukraine ....................................................................... 49
Table 13. MNL of vehicle adoption in Brazil .......................................................................... 50
Table 14. The relationship between the studied factors, RP acceptability, and future car choice
in the four investigated countries ............................................................................................. 53
Table 15. Summary of GEH Values ......................................................................................... 60
Table 16. SBA reaction time factor values .............................................................................. 62
Table 17. Utility function factors values for private transport ................................................. 64
Table 18. VKT [km] in the network during AP ....................................................................... 76
Table 19. Wilcoxon signed-rank test results for investigated traffic performance parameters 76
Table 20. Link-based Scheme toll criteria and calculation method ......................................... 85
Table 21. The impact of the RP strategies on volume, VKT, and velocity on the link's level 91
Table 22. Wilcoxon signed-rank test results for tested variables ............................................. 92
Table 23. Consumer Surplus and Generated Revenues [Million HUF] ................................... 93
List of Figures
Figure 1. Analytical Framework .............................................................................................. 43
Figure 2. Mean Values of respondent's preferences for using RP revenues ............................ 44
Figure 3. Respondent's trust in their governments regarding the use of RP revenues ............. 44
Figure 4. Graphical representation of the sections used for the calibration and their
corresponding GEH values ....................................................................................................... 61
Figure 5. The service area and the walk network for SAV ...................................................... 68
Figure 6. The location of holding areas and Pu/Do points within the SAV service area ......... 69
Figure 7. Simulation area, including the Budapest network and its surroundings ................... 70
Figure 8. SBA queue length (summation of average and maximum of max) @ 8:00 AM for all
scenarios ................................................................................................................................... 72
Figure 9. Delay in each scenario during the AP and Percentage change of traffic delay for
three proposed future scenarios compared to Base scenario .................................................... 73
Figure 10. Total volume in each scenario during the AP and Percentage change of traffic
volume for three proposed future scenarios compared to Base scenario ................................. 73
Figure 11. Average traffic density for all scenarios ................................................................. 74
Figure 12. The change in SBA utilization after deploying AVs and SAVs compared to the
Base scenario ............................................................................................................................ 74
Figure 13. Average velocity in each scenario during the AP and Percentage change of traffic
velocity for three proposed future scenarios compared to Base scenario ................................ 75
Figure 14. Consumer surplus changes for three proposed future scenarios compared to Base
scenario ..................................................................................................................................... 77
Figure 15. Budapest's planned toll bridges............................................................................... 84
Figure 16. Distance-based tolling scheme area ........................................................................ 85
Figure 17. Selected links for tolling in the Link-based Scheme .............................................. 86
Figure 18. Tolled links and average toll value at each ATI for all scenarios in Link-based
Scheme ..................................................................................................................................... 87
Figure 19. Percentage change of traffic delay for all scenarios according to the RP scheme .. 88
Figure 20. SBA queue length (summation of average and maximum of max) @ 8:00 AM for
all scenarios according to the RP schemes, where NT: No Toll (Base case scenario), BT:
Bridge Toll, DT: Distance-based Scheme, LT: Link-based Scheme ....................................... 89
Figure 21. The change in SBA utilization after applying RP strategies compared to the "No
Toll Case" for all scenario ........................................................................................................ 90
Figure 22. The change in volume, VKT, and velocity after applying RP strategies compared
to the "No Toll Case" ............................................................................................................... 92
Figure 23. Welfare changes for RP strategies for each scenario .............................................. 94
List of Abbreviations
Abbreviations Explanation
Base Year’s Motorization Degree;
Motorization Value throughout the Given Year;
Number of Trips before AVs and SAVs Emergence;
Number of Trips after AVs and SAVs Emergence;
Utility function for Vehicle Drivers;
Growth Rate;
Trip’s Generalized Cost before AVs and SAVs Emergence;
Trip’s Generalized Cost after AVs and SAVs Emergence;
Variable Sensitivity Factor;
∆CS Change in Consumer Surplus;
ADT Acceptable Detour Time;
AP Analysis Period;
ASC Alternative-specific Constants;
AT Arrival Time;
ATI Analysis Time Interval;
AVs Autonomous Vehicles;
BKK The Centre for Budapest Transport;
BT Bridge Toll;
C SBA Capacity [veh/hour];
CC Conventional Cars;
CFLP Capacitated Facility Location Problem;
CS Consumer Surplus;
DF Detour Factor;
DRS Dynamic Demand-Responsive Ride-Sharing System;
DT Distance-based Scheme;
EDT Earliest Departure Time;
EFM Macroscopic Transport Model for Budapest and the Agglomeration;
GEH Geoffrey E. Havers Function;
HUF Hungarian Forints;
IDT Ideal Travel Time;
KMO Kaiser–Meyer–Olkin;
LT Link-based Scheme;
MDT Maximum Detour Time;
MLR Multiple Linear Regression;
MNL Multinomial Logit Model;
NT No Toll (Base Case Scenario);
O-D Origin-Destination;
PC Parking Charges;
PCA Principal Components Analysis;
PCU Passenger Car Unit;
PrTSys Private Transport System;
PT Search Time for Parking;
Pu/Do Pick-up/ Drop-off Points;
RoH Rule of Half;
RP Road Pricing;
S Motorization Concentration Degree (Saturation);
SAVs Shared Autonomous Vehicles;
SBA Simulation-based Dynamic Traffic Assignment;
tcur Travel Time;
TPP Traffic Performance Parameters;
TSys Transport System;
V2I Vehicle-to-Infrastructure;
V2V Vehicle-to-Vehicle;
VDF Volume Delay Function;
VKT Vehicle Kilometer Traveled;
VOTT Value of Travel Time;
VRP Vehicle Routing Problem;
WT Time Used in Walking;
Ͳ Distance-based Toll Value;
SBA Effective Vehicle Length [m];
Trip’s Generalized Cost Function;
Link Density [Veh/km];
Number of Lanes;
Traffic Flow [Veh/hour];
SBA Reaction Time Factor;
Number of Years since about the Base Year;
Travel Speed [km/hour];
Link’s Velocity [km/h].
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Chapter One - Introduction
1.1 Background
The complexity and global nature of the mobility challenges, like the problem of
congestion, is a global issue and significant challenge for many cities, especially when
considering the environmental aspects and the need for implementing sustainable transport
policies. Some of the reasons for congestion are the massive increment in the number of private
cars during the last few decades (i.e., growing motorization rates), the inadequate infrastructure,
the travel behavior of road users (i.e., using personal vehicles in traveling more than public
transport and soft mobility), and the lack of comprehensive strategic plans to manage
transportation. As a consequence of traffic congestion, many problems start to occur, such as
pollution (visual pollution, air pollution, underground water pollution, and noise). Likewise,
the extra travel times during congestion lead to more fuel consumption, bad public mood, and
more losses in terms of money. Similarly, congestion leads to higher accident rates.
The advancement in automotive technology may contribute to solving some mobility-
related issues, such as introducing self-driving vehicles as a mobility alternative to the present
transportation means, which is expected in the future. The vehicle industry, policymakers, and
academia pay attention to rapid global research and innovation connected to autonomous
vehicles (AVs), shared autonomous vehicles (SAVs), and their projected appearance on
roadways. Due to their benefits, AVs and SAVs are projected to dominate automobile markets
[1]–[4]. Additionally, several laws relating to the deployment of AV and SAV have been
effectively implemented in many nations and areas [5], [6].
The benefits of automated vehicles are predicted to be significant, notably in terms of
traffic safety, energy usage, and accessibility [7]. In terms of traffic, AVs and SAVs will assist
in relieving congestion by lowering the number of accidents due to human mistakes, shortening
headways, and optimizing the use of intersections [8], [9]. Furthermore, as these vehicles do
not require human interaction to finish the journey, users of AVs and SAVs may better use their
travel time by doing other activities like studying or relaxing instead of monitoring the road [9],
[10]. Nevertheless, AVs and SAVs are anticipated to increase the number of trips and mileage
driven by providing more access to motorized transport to new groups of users, e.g., those who
were previously unable to travel by car owing to various considerations like age or disability,
resulting in increased traffic [7], [11]. The improved accessibility would change the travel
demand globally, where people worldwide will have more access to motorized transport due to
the advent of AVs and SAVs. As a result, the impacts of AVs and SAVs on road congestion
are yet unclear, and they may exacerbate existing traffic issues [3], [12]. Consequently, there is
a foreseen need for regulatory traffic policies to alleviate congestion-related problems in the era
of AVs and SAVs [13].
Road pricing (RP) is one of the most effective and efficient travel demand management
tools for mitigating traffic-related problems such as congestion [14]–[16]. Moreover, it has been
adopted in several cities around the world, such as London (UK), Milan (Italy), and Singapore,
and is spreading to others [17]. For instance, Ultra Low Emission Zone (ULEZ) was
14
incorporated into the area-based pricing regime in London to promote sustainable travel and
comply with air quality standards of the Europeans Union [18], which helps improve air quality
and increase bicycle demand [19], [20]. Furthermore, the application of more economically
efficient and behaviorally effective RP strategies is possible in the era of AVs and SAVs due
to increased communication and location capabilities and rapid information exchange [9], [21].
Therefore, the use of RP strategies could be one of the optimal options for counteracting the
potential increase in congestion due to the emergence of AVs and SAVs, and the RP policies
shall be revised and modified to suit AVs and SAVs.
However, there is an obstacle that faces the implementation of such a scheme; that is
RP acceptability [22], [23]. The motorists are not used to paying for using the road; moreover,
they consider it a preserved right to use the road freely [24]. For instance, the RP scheme failed
in Netherland, Copenhagen, and Edinburg due to the lack of acceptability [25]. One important
aspect of SAVs is that they are expected to emerge as a demand-responsive service [26].
Moreover, shared mobility services are predicted to prosper in the future and provide the first
appearance of self-driving cars in the frame of SAVs because of their rising popularity and
cheaper travel costs compared to privately owned vehicles [27]. This is especially true with
electric SAVs, which are expected to be widely adopted and provide a more viable society [27]–
[30]. Thanks to the evolution of information and communication technology and mobile
solutions, ride-sharing services have become more popular in several cities [31]. Such services
provide users with cheaper and more versatile commuting options. Moreover, they are
associated with lower vehicle ownership and greenhouse gas emissions [32]–[34].
1.2 Motivation and Research Novelty
A revision of the current state of the literature concerning AVs and SAVs showed that
technological advancement in the field of motorized transport accelerates swiftly, especially in
the advancement of self-driving vehicles [3], [35]–[37]. Accordingly, it can be stated that given
the massive automakers’ continued investments in AV technology, self-driving vehicles are
anticipated to have a high share of the future automotive market and would probably be
important enough to require the complete focus of the policymakers and transport specialists
[1], [2], [4], [38]. Despite the expected benefits of AVs and SAVs on traffic performance,
accessibility, environment, accident rates, and fuel efficiency [7], [9], [10], [39]–[42], it is
expected that the travel demand will increase, especially in Europe, due to the higher
accessibility for motorized transport modes leading for more and longer vehicle trips [7], [11],
[12], [43], [44]. The increased travel demand may exacerbate traffic congestion, necessitating
the use of techniques for managing travel demand to reduce the adverse effects of self-driving
vehicles [38], [45]. There is a valid opportunity to control the induced demand through RP
schemes, where toll values can be adjusted to dissipate the induced demand. Thanks to the
modern technology in AVs and SAVs, a more dynamic RP scheme can be implemented in the
era of AVs and SAVs, as can be seen in several research articles that discussed the possible
approaches to designing different road pricing strategies in the world of self-driving vehicles
using different transport modeling techniques [21], [29], [44], [46]–[51].
15
Therefore, RP is anticipated to be an effective tool in mitigating traffic-related issues in
the era of AVs and SAVs. This dissertation investigates the possible approaches to applying
RP successfully and efficiently in the presence of AVs and SAVs. For a successful
implementation of RP, the key requirement is the public acceptability of RP. Although there is
a vast amount of literature on RP acceptability and self-driving car adoption, there is no study
yet that has investigated RP acceptability in connection with the adoption of AVs and SAVs
and the factors influencing them. To the best of my knowledge, this dissertation is the first to
cover this research gap by introducing studies on RP acceptability and AV and SAV adoption
while also examining the impacts of various factors on both. The development and realization
of RP policy and driverless vehicles will contribute to the process of sustainable development,
especially in terms of economic, environmental, and socially sustainable development. The
successful implementation of RP and the advent of AVs and SAVs would help overcome
challenges such as air pollution and climate change by reducing the amount of traffic and
promoting sustainable development.
On the other side, for an efficient application of RP in the era of AVs and SAVs, I
utilized a simulation-based dynamic traffic assignment (SBA) using a transport network model
for Budapest (EFM Model) within the traffic macroscopic simulation software "Visum" to
integrate AVs and SAVs to Budapest network and apply different RP strategies (static and
dynamic) in alternative future traffic scenarios for the years 2030 and 2050. I analyzed the
implications of including self-driving cars and implementing RP strategies on network
performance, consumer surplus (CS), and social welfare. This dissertation presents a
methodological framework for deploying AVs, SAVs, and various RP strategies using SBA,
which will contribute significantly to modeling projects of related research and can help
creating the dynamic traffic modeling of such elements using software like Visum. Several
essential characteristics that are anticipated to be crucial in the context of AVs and SAVs were
considered, such as vehicle reaction time and headway, vehicle-to-infrastructure
communications, time constraints for the SAV to pick up a request, in-route check, and
acceptance of other trip requests based on determining factors, and vehicle power level.
Moreover, the introduction of an on-demand travel service for SAV can have a hand in crafting
an advanced shared mobility system, and the use of real-time simulation can give the
opportunity for the application of advanced RP strategies, which are expected to be introduced
and used in the presence of AVs and SAVs due to the advanced technology that driverless
vehicles possess.
Furthermore, this dissertation aims to highlight the factors that will affect RP
acceptability and self-driving vehicle adoption, as well as the implications of introducing AVs
and SAVs and applying different RP strategies in alternative future traffic scenarios on traffic
performance and economic changes. Results of this dissertation can provide meaningful
insights to stakeholders and policy makers for anticipating and planning policy controls related
to the expected impacts of RP, AVs, and SAVs in the transportation regime.
16
1.3 Research Questions
As mentioned in the above sections, RP presents a solution for the expected change in
travel demand due to the advent of AVs and SAVs. However, on the one hand, it is necessary
to adjust the use of RP strategies by utilizing the advanced technologies in self-driving vehicles
to ensure an efficient application that guarantees to tackle the expected upsurge in travel
demand. On the other hand, the obstacle of RP's low acceptability level needs to be investigated
to ensure the successful implementation of RP schemes.
Consequently, the scope of this dissertation covers the possible methods and policy
implications that help raise the RP acceptability and self-driving vehicle adoption through
questionnaires distributed worldwide, as the problem is global. Then, it focuses on the
implications of the emergence of AVs and SAVs and the impacts of applying different RP
strategies in the era of self-driving vehicles by utilizing a macroscopic traffic simulation tool to
shed light on the possible approaches for efficient application of RP in the time of AVs and
SAVs. The dissertation answers the following research questions.
Q1: Do the RP schemes consider an acceptable global measure to manage the demand
for self-driving vehicles?
Q2: Which factors significantly affect RP acceptability and self-driving vehicle
adoption for developing a successful RP scheme in the era of AVs and SAVs?
Q3: What effects do AV and SAV deployments have on a societal level, personal
level, and traffic-sensitive areas?
Q4: How does varying the share distribution of AVs and SAVs affect traffic
performance and economic aspects?
Q5: To what extent are the RP schemes considered efficient global travel demand
management tools in managing the demand for AVs and SAVs?
Q6: What are the impacts of applying different RP strategies in the time of self-
driving vehicles on traffic performance, society level, and personal level?
Q7: What policy implications can be derived from applying the RP strategies in the
era of self-driving vehicles to improve network performance and increase social
welfare?
17
1.4 Used Scientific Methods
I utilized a variety of scientific methodologies in conducting my research. A brief
summary of the scientific methods used in my research is presented in this section.
I utilized a dynamic traffic assignment (i.e., SBA) using a transport network model
within the traffic macroscopic simulation software "Visum" to integrate AVs, SAVs, and the
pricing strategies to the network and conduct the modeling work. The process includes
integrating new transport systems (i.e., AVs and SAVs) into the network, extending the SBA
assignment method with reacting to AVs special features, modifying the network to be S/AV-
ready to distinguish automated vehicles from conventional ones by identifying the
characteristics of each transport system separately across the network, adjusting the input
parameters required for the car following model to model AVs and SAVs, defining the changes
to private transport demand matrices according to the future traffic scenarios, designing a ride-
sharing system for SAV within the model using the dynamic demand-responsive ride-sharing
system (DRS) and vehicle routing problem (VRP) to serve the maximum number of trip
requests utilizing the available fleet of vehicles, adjusting the SBA parameters to get a realistic
results, and finally, defining the applied pricing strategies within the model.
As the SAV ride-sharing model is a real-time sharing system, the dispatcher method is
used in tour planning to solve the vehicle assignment problem. As I applied the dynamic traffic
assignment (i.e., SBA) method instead of static equilibrium assignment (used in the original
model), I performed an additional calibration and applied all necessary changes to ensure the
model's suitability for the use of dynamic traffic assignment. I used GEH statistics based
calibration in the modified model because it has a property that the relative deviations decrease
with increasing observed values, and it places more emphasis on larger flows than on smaller
flows, which represents the situation of the case study’s transport network [52], [53].
Additionally, GEH statistic is adopted by different road administrations, such as FHWA in USA,
BKK in Hungary, and ARRB in Australia [54], [55]. I conducted different statistical analysis
approaches to analyze the obtained data, including descriptive statics and non-parametric tests.
As the data did not fit a normal distribution, I applied Friedman with Bonferroni correction tests
for all possible combinations to identify significant differences between different scenarios and
Wilcoxon signed-rank tests for pairwise comparison of all combinations which had different
distributions according to the Friedman test. Furthermore, I utilized cost-benefit techniques for
the economic appraisal by defining the changes in social welfare and consumer surplus.
I formulated questionnaires by extending well-known methodologies to bridge a
research gap by simultaneously investigating the acceptability of RP, AVs, and SAVs. I used
different econometric models to analyze the received data from the distributed questionnaires
to provide insight into the public perception of RP, AVs, and SAVs. For instance, a
multicollinearity problem exists between the measured variables in questionnaire responses
with a large dimension [56]. Thus, working with lower dimensionality without sacrificing much
information is desirable, meaning that dimensionality reduction is critical [57]. One of the most
commonly utilized techniques for this is factor analysis. One or more indicators are used to
determine the number of factors. It is not always easy to determine the right number of factors
18
to maintain a decent explanation of the data. As a result, I used principal component analysis
(PCA) with varimax rotation [58] to determine the factors' numbers.
I used multiple linear regression (MLR) to investigate the influence of independent
variables on the dependent variable. Similarly, with many other statistical analyses, I checked
a set of assumptions prior to the analysis (e.g., the linear relationship between the dependent
and independent variables, normally distributed error terms, and no multicollinearity between
the independent variables). Following the verification of the assumption, then applying a
multiple regression is valid. The general formula of the linear regression model is expressed as
shown in Equation 1.
(1)
where is the dependent variable; : i = 1, ….., is the number of the independent
variables considered in the model; : i = 0, . . . , is the regression coefficient, and is the
error term.
Multinomial Logit Model (MNL) is used when there are two or more discrete-
categorical dependent variables. In my research, the respondents were asked to make a trade-
off among three unordered and discrete variables (i.e., AVs, SAVs, and CC). Therefore, MNL
is estimated. I used linear-additive utility specifications to create the utility structure for all the
estimated models. The general form of the utility function is presented in Equation 2. The utility
is composed of four parts: the first part, is the alternative specific constant (ASC) which
represents the mean of all unobserved resources of the utility. The second part captures the
utility derived from latent variables; is a vector of parameters which is pertaining to the
attitudinal factors k. Xjk is a vector of latent variables derived from factor analysis, where k=
1,2,….., k number of variables. The third part comprises the utility formed by the
sociodemographic variables of the individuals. is the estimated parameter for each
sociodemographic variable and is a set of sociodemographic variables. The fourth part
represents the unobserved component which is associated with individuals’ idiosyncrasies as
well as the error by the analyst; it is assumed to be independently and identically distributed
(IID). The probability of the MNL model is shown in Equation 3. Further statistical techniques
were used in analyzing the obtained data, such as descriptive statistics and bivariate analysis.
(2)
(3)
19
1.5 Dissertation Outline
The dissertation consists of six chapters.
Chapter one introduces the research topic, explains the research motivation and novelty,
defines the research questions, outlines the applied scientific methods, and displays the research
sequence.
Chapter two utilizes well-known methodologies in defining the RP acceptability and
factors affecting it, where I distributed a questionnaire globally and analyzed the obtained data
to answer question #1 mentioned in Section 1.3.
Chapter three further develops the used methodologies in chapter two and introduces a
new model to study the interrelationship between RP acceptability and self-driving vehicle
adoption, where I distributed a questionnaire in four countries and analyzed the obtained data
to answer question #2 mentioned in Section 1.3.
Chapter four integrates AVs and SAVs into a validated transport network model within
the traffic macroscopic simulation software "Visum" in alternative future traffic scenarios to
investigate their impact on a societal level, personal level, and traffic-sensitive areas (questions
#3 and #4 mentioned in Section 1.3). I introduced three future traffic scenarios that reflect
different possibilities of the market share of AVs and SAVs for the years 2030 and 2050.
Chapter five applies static and dynamic RP strategies to the developed future traffic
scenarios using dynamic traffic assignment to answer questions #5 to #7 mentioned in Section
1.3.
Chapter six, finally, sheds light on the new scientific findings, the practical use of the
theses and outlook, and the potential scope for future research work on this subject.
20
2 Chapter Two - Urban Road Pricing Acceptability: An
International Comparative Study
2.1 Introduction
Several economists and specialists in transportation consider road pricing (RP) as a
measure that can be used successfully to reduce congestion and its related problems [16].
Implementation of an RP scheme in London, Stockholm, and Milan showed promising results,
e.g., the traffic entering the congestion zone was decreased during charging hours by 18%, 18%,
and 14.2%, respectively [59]. However, there is an obstacle to the implementation of such a
scheme; that is public acceptability [60], [61]. Motorists are not used to paying for road use;
moreover, they consider it a preserved right to use the road freely [62], [63]. For instance, the
RP scheme failed in the Netherlands, Copenhagen, and Edinburg due to the lack of acceptability
[64]. However, the low level of acceptability can be raised if the authorities properly introduce
the congestion charging scheme by providing enough alternatives for road users like adequate
public transport (PuT) services to the congestion zones and using of revenues properly, as in
the case of Stockholm and Milan [65].
The European Union funded a large project called “Acceptability of Fiscal and Financial
Measures and Organisational Requirements for Demand Management” (AFFORD), which
included defining the factors that influence RP acceptability in four European cities (Athens,
Como, Dresden, and Oslo) [66]. A replication of this study was also conducted in Vienna [67],
[68]. The current research follows their approach by partially applying the survey in five
capitals around the world (Budapest, Hungary; Tunis, Tunisia; Amman, Jordan; Ulaanbaatar,
Mongolia; and Damascus, Syria). The reasons for choosing this model are its acceptance in the
scientific field as it was replicated in other cities (e.g., Vienna) and the well-established
procedure followed in the mentioned research; moreover, the next chapter of this dissertation
includes the development of the mentioned model to study the impact of several factors on RP
acceptability in the era of self-driving cars.
The proposed strategy in this research was designed adequately by checking the
previous studies about RP in the selected capitals, comparing it with any existing scheme within
each country, and consulting with experts and specialists in transportation and economics to
validate the chosen values of the amount of the tolls. Accordingly, it is assumed to avoid any
bias in the reported results, which may occur as a result of an aggressive strategy or
underestimated one.
This study differed by dealing with a cross-country survey inside Europe (i.e.,
Budapest) and outside (i.e., East Asia: Ulaanbaatar, North Africa: Tunis, Middle East: Amman
and Damascus). The results provide a substantial base for finding the factors affecting the RP
acceptability. Moreover, it helps in assessing the usability of the underlying model by
comparing the main findings of the previous studies with this research results, especially since
the last three mentioned studies (i.e., the AFFORD and Vienna projects) were conducted only
in European cities.
Some of the selected cities (e.g., Amman, Tunis, and Damascus) share similarities such
as the share of transport modes, socio-economic characteristics, and residents’ habits. Moreover,
21
in the past few years, both Tunis and Damascus have been engaged in an analogous political
situation, the so-called “Arab Spring”. On the other hand, the selected cities, Budapest,
Ulaanbaatar, and the other three, also have many differences. Therefore, introducing this study
on a large scale will provide a comprehensive understanding of the factors affecting the
acceptability of the RP among different societies with different conditions.
The major contribution of this thesis is threefold: firstly, it measures the current level of
the acceptability of RP in the surveyed cities. Secondly, it identifies the main factors affecting
the acceptability of the congestion charging scheme in the studied cities. Thirdly, it provides a
broader scope for testing the underlying model in different environments and societies.
The chapter is organized as follows. Section 2 provides a comprehensive literature
review on RP acceptability. Section 3 summarizes the theoretical background behind the
selected factors. The methodology and description of the statistical analysis are presented in
Section 4. After presenting the results in Section 5, the discussion and conclusions are provided
in Section 6.
2.2 Literature Review
Extensive research has been carried out on the acceptability of RP since the late eighties.
This section recapitulates the literature related to RP acceptability.
Since Pigou introduced the concepts of negative externalities and corrective tax about a century
ago [69], many economists have emphasized the efficacy of RP in alleviating traffic-related
problems [14], [15], [70], [71]. Despite the sound economic theory behind RP, it has low public
and political acceptance, which hampers its introduction and international spread [72]. Some of
the reasons behind its low acceptability are that the aims of implementing RP can also be
achieved by taking alternative measures like improving PuT or using access restriction rules.
Moreover, RP is often perceived as unfair because it is a new tax supplementing existing taxes
[73].
Several studies have developed different models to study the impact of individuals’
attitudes, behaviors, and characteristics on RP acceptability. Verhoef et al. gathered data using
a survey on RP from road users in Randstad, the Netherlands, during morning peak hours. The
results showed that their willingness to pay to save trip time significantly depends on their
income level and the compensations offered to users for paying the toll [74]. Likewise, a similar
study in the Netherlands by Rienstra et al. concluded that the effectiveness of the proposed
policy and problem perceptions influence the acceptability of transport policy measures.
Moreover, participants with certain socio-demographic characteristics supported the transport
policy measures; for instance, a higher level of education was shown to positively impact
acceptability, while having a car had a negative impact [75]. Strikingly, income level did not
show a significant influence on the acceptability of the transport policy measures. In contrast,
a study investigating congestion charging acceptability in Athens showed that respondents aged
from 35 to 64 years old with high household income tended to travel through the charging areas
using a passenger vehicle [76]. An analysis of survey data that investigated various transport
policies in Texas by Kockelman et al. revealed that the implementation of congestion pricing
would likely motivate the elderly to decrease their travel and large household members to alter
their travel routes, while full-time employees are less likely to change their travel patterns [77].
22
Attitudinal surveys were the most frequently used method to explore the factors that
affect RP acceptability. Jones performed twelve surveys in the United Kingdom on the public
perception of traffic-related issues and support for various pricing measures. However, the
analysis of the data did not reveal interdependence between the proposed measures’ acceptance
and the examined factors [78]. This gap was addressed by Schade & Schlag, who investigated
RP acceptability levels in four European cities, as well as the relationship between the
investigated factors and the acceptability of RP in particular [61], [66]. The latter model was
replicated in Vienna [67], [68] and five capitals around the world by Shatanawi et al. [79]. Some
of the key findings from the four previously mentioned studies include the significant positive
relationships between RP acceptability and the three predictors: “personal outcome
expectation”, “social norm”, and “perceived effectiveness”. A stated preference experiment was
carried out in London and Leeds by Jaensirisak to evaluate the impact of different aspects of
scheme design, such as the value of charges, location and period of charging, type of charging,
and utilization of revenue on acceptability. The key findings of the study were that non-car
users who considered congestion and environmental problems as serious and perceived RP as
an effective measure of alleviating these problems were more receptive to the scheme’s
implementation. Furthermore, it confirmed that the impact of attitudinal variables on the
acceptability of RP is greater than the impact of socio-demographic characteristics [80].
Public opinion is a critical determinant of any new technology or policy, and RP is no
different. Hensher and Li reviewed voting behavior in various RP referendums in cities such as
Stockholm, Milan, and Edinburgh. They concluded that lack of knowledge about the RP
scheme and uncertainty about its effectiveness were the main reasons for voting against its
implementation. To successfully implement RP schemes and gain public support, they
proposed developing a forecasting model during the design phase to inform the public about
expected changes and introducing a trial period preceding a referendum, later evaluating and
displaying the outcomes to the public through media [81]. The latter point is consistent with the
findings of Winslott-Hiselius et al., who analyzed the effect of the Stockholm congestion trial
on public attitudinal changes toward the proposed RP scheme [82]. The findings are also
consistent with Gu et al., who claimed that one of the possible reasons for the rejection of the
proposal to introduce a congestion pricing scheme in New York City (in 2007) was the lack of
an RP trial [83]. In addition, the media also plays a vital role in affecting public opinion; for
instance, newspapers in Edinburgh reported negative coverage of the introduction of the RP
scheme, which led to its rejection in the 2005 referendum [84]. On the other hand, the
abundance of information announced regarding the effectiveness of the RP schemes
implemented in Stockholm, Milan, and London had a major role in their success and acceptance
by the public [83]. A review of public opinion data before and after the implementation of RP
in California, Texas, and Minnesota by Ungemah & Collier revealed that the public support for
applying tolls increased over time in the case of the SR-91 Express Lanes and I-15 FasTrak
High Occupancy Toll (HOT) Lanes in California and especially after their implementation [85].
For more information about the SR-91 Express HOT Lane and other HOT lanes in the U.S., see
[72].
Palma et al. summarized the results of public opinion surveys concerning RP. They
found that the allocation of revenues from RP to explicit and particular uses within the transport
sector increases the public acceptability of RP [72]. Several studies have discussed the optimal
distribution of revenues. Small suggested returning 70% of the revenues to commuters and
23
earmarking 30% to improve the transportation system [86]. Estimation of a bivariate probit
model applied to the responses collected from Southern California residents by Harrington et
al. showed that allocation of the revenues collected by implementing RP to reduce other taxes
increased the acceptability of RP by 7% [87]. Farrell and Saleh recommended using the
revenues from RP to enhance the PuT system by improving ticketing systems and reliability by
offering more discounts, expanding the network coverage, and providing real-time information
[88]. Ubbels and Verhoef stated that the acceptability of RP will increase if the revenues are
allocated to reduce the fuel tax and abolish car taxation [89].
Equity was identified as one of the key public concerns regarding RP by Kocak et al.
due to the scheme’s potential to cause economic or social disturbances, constrain the free
movement of individuals and goods, and threaten the economic viability of companies in RP
zones [90]. These concerns are reflected in a study by Sun et al. on the impact of several
variables affecting RP acceptability in China [91]. The RP scheme can be made more equitable
and fair, according to Hao et al., by offering low-income and short travel distance groups a
transportation subsidy, as well as by providing the low-income and long travel distance groups
a tax subsidy, thus taking vertical equity into consideration. Horizontal equity can be achieved
by using part of the tax for passenger benefits and lowering taxes on users’ vehicles [92]. The
distributional effects of nine different RP schemes on commuters in Paris were simulated and
compared using an econometric model developed by Bureau and Glachant. A key finding was
that equity patterns are influenced by the level of traffic reduction and the RP scheme design
[93].
2.3 Theoretical Background
This section investigates the latent variables which may affect the acceptability of RP.
Many of them were drawn from a previously developed heuristic model [61], [66], [94], [95]
and are based on the theory of reasoned action and planned behavior [96], [97]. Ajzen's “Theory
of planned behavior” aimed to anticipate the behavior of people in life's different aspects and
believed that most patterns of social behavior are consciously controlled. On the other hand,
Fishbein and Ajzen’s “Theory of reasoned action” focuses on the relationship between
behaviors and attitudes with regard to actions that determine a person's behavior [68]. The latent
variables were derived from the before-mentioned model due to its coherent methodology, clear
concept of definitions, detailed research framework, and the acceptance of the method by the
scientific community as reflected in its use in various publications [67], [68], [79]. The term
acceptability refers to a potential decision regarding a hypothetical measure that would be
presented in the course of time [61]. The following factors and their expected impact on the
acceptability of RP are described below.
• Sensing Traffic-Related Problems
People who understand the implications of traffic-related problems are more open to
accepting measures or policies that intend to mitigate their adverse effects [61]. However,
according to empirical findings, this approach is not fully confirmed and needs to be ascertained.
For example, stakeholders in Spain refused an RP measure despite perceiving traffic-related
issues as serious problems [98]. Rienstra et al., on the other hand, found a relationship between
the acceptability of policy measures and problem perception, with the public supporting policy
measures that improve safety, the environment, or reduce congestion [75].
24
• Awareness
People have lower levels of information about pricing measures like RP compared to
other demand management measures like "improving PuT". Lack of knowledge about RP
results in a lower acceptability level [95]. The hypothesis is that those with more information
about the RP scheme will be more receptive to its implementation due to a higher awareness of
its benefits and effectiveness. However, this is not always the case because a high level of
information may lead to higher assessment and consequent rejection of the scheme [61].
• Effectiveness
Perceived effectiveness represents the extent to which the policy objectives are achieved.
For example, if the RP scheme is implemented in a city to achieve specific aims such as
improving air quality, higher expectations of achieving the goals will result in higher
acceptability of the scheme [61]. This implies a positive relationship between the acceptability
of a measure and its perceived effectiveness [66], [68], [99]. However, people may justify their
refusal of a coercive measure or policy by evaluating it as ineffective in the context of a strategic
response [75]. This research makes a distinction between perceived effectiveness and personal
effectiveness. The latter represents a change in travel behavior due to the application of the RP
scheme; reducing the number of trips using personal cars after RP implementation is an
example of personal effectiveness [75], [89].
• Social Norm
The social norm is a social factor that refers to the “perceived social pressure” to comply
with certain behavior, where social pressure is defined as the perceptions, beliefs, and
judgments of other households and community members. Both attitudes and social norms are
grounded in the belief systems of an individual [100], [101]. For instance, if close relations such
as family or friends favor implementing a specific policy measure, this will create a positive
social influence on the person to accept the same measure. Hence a policy or measure has a
higher probability of being accepted if the social environment accepts it [60], [66].
• Travel Behavior and Attitudes
Commuters have different travel habits, and they look for different goals. This research
compares the positive traveling attitude, which includes collective social benefits in the long
run (e.g., “I want better air quality in the city”), and the negative travel attitude, which
concentrates on the personal outcome in the short run. This comparison is derived from the
concept of social dilemmas [102], [103]. The assumption is that if commuters have a more
positive traveling attitude, they will be more open to accepting congestion charging because of
its benefits to society. In contrast, the ones with a negative travel attitude will most likely reject
it as it restricts their mobility freedom and costs them more money.
• Ascription of Responsibility
Defining who is responsible for causing the traffic-related problems and who is
responsible for solving them is related to the acceptability of RP. The hypothesis is that if people
feel partially or entirely responsible for causing these problems, they feel that they should take
part in solving them, and their acceptance level will be higher. Similarly, those who consider
the government or others as responsible for these issues have lower acceptability [91], [104],
[105]. However, other studies showed that the feeling of responsibility does not significantly
affect the acceptability of a pricing measure, and this factor is considered negligible [98], [106].
25
This research examines responsibility sharing between the authorities and individuals according
to the respondent’s perspectives and correlates the responsibility factors with the acceptability
of the RP.
• Socio-Demographic Characteristics
Schade & Schlag argued that socio-demographic characteristics might influence the
acceptability of RP [61]. For instance, higher income groups should be more interested in the
implementation of RP than lower income groups [75]. Moreover, other researchers found a
relationship between socio-demographic characteristics and variables which might affect
acceptability. For example, Wang et al. concluded that “gender, age, and education level have
a significant effect on the perceived uncertainty about effectiveness and fairness of congestion
charging” [107]. Conversely, the direct impacts of personal features on the acceptability of RP
were found to be rather low in some studies [60], [75]. On the other hand, other studies have
found a relationship between age and adoption level of AVs and SAVs (e.g., younger travelers
are more likely to use SAVs) [26], [108].
2.4 In-depth Analyses
This section presents an overview of the survey distribution, the data analysis methods,
and the proposed RP strategy for the respondents.
2.4.1 Sampling
The questionnaires were distributed randomly in the five capitals using Google Forms.
A time filter was considered to validate the responses, and responses completed within 10
minutes or less were eliminated. The overall sample number was 1229. The study was based
on an online survey tool and answered by those who have internet access; therefore, it may not
be representative of the distribution of populations. However, the online surveys were the
optimal approach in this research as they are faster, cheaper, and more convenient, especially
once considering the number and locations of the capitals of interest. In Amman, data were
collected during March and April 2018 and in the other four cities simultaneously from
November 2018 to January 2019.
2.4.2 Quantitative Data Analysis Methods
Survey data with a high dimension is subject to a multicollinearity problem between the
measured variables [109]. In addition, it is preferable to deal with fewer dimensions without
losing much information. Accordingly, dimensionality reduction is important but critical [57].
A multivariate statistical approach factor analysis is one of the most popular methods used for
this purpose. It describes the variability among the correlating observed variables through a
lower number of uncorrelated latent variables (known as hidden variables or factors) [110]. The
number of these hidden variables/factors is obtained from one or more indicators.
Specifying the appropriate number of factors (K) that retains an adequate explanation of the
data is not usually obvious. Therefore, principal component analysis (PCA) helps in setting this
number [111]. Besides, the statistic called “Cronbach’s Alpha” measures the internal
consistency of the amalgamation of several questions into one factor. Furthermore, the Kaiser–
Meyer–Olkin (KMO) statistic measures the data adequacy for conducting factor analysis [110].
26
Another basic and common multivariate statistical method is multiple regression modeling.
This technique is a predictive analysis that allows researchers to study the relationship between
one or more dependent variables and the “p” number of independent variables. After checking
the model assumptions (the linear relationship between the dependent and independent
variables; the error terms are normally distributed and at the same time independent of the
explanatory variables; no multicollinearity between the explanatory variables), constructing a
multiple regression is legitimate. The regression model equation is as follows.
where is the dependent variable; : i = 1, …., is the number of the independent variables
considered in the model; : i = 0, . . . , is the regression coefficient that measures the change
of the dependent variable according to the change of the explanatory variables, and is the
error term (residual), which measures the difference between the predicted and observed values
of the response variable.
The data analysis started by conducting descriptive statistics (percentage distribution
contingency tables/bivariate analysis) to create a profile for each city and compare it between
the cities. Then, the factor analysis technique was used to create outcome measurements.
Finally, the multivariate analysis (multiple regression) was considered to examine to what
extent the outcome factors affect the public acceptability of RP scheme within each city.
2.4.3 The Proposed Strategy
The proposed strategy aims to reduce traffic-related problems such as congestion, air
pollution, etc., where the movement of the vehicles will be controlled through cameras
registering the vehicle’s movement (in and out) of the restricted zone. The charging areas
“restricted zones” were not precisely defined in each city to avoid uncertainty resulting from
respondents’ unfamiliarity with a specified zone. Moreover, the suggested fees vary according
to the time of the day, as shown in Table 1. On the one hand, the amount of fee was suggested
according to (1) previous research proposals (e.g., [112] (p. 51)); (2) similar implemented toll
charging schemes (e.g., in Tunisia, motorists are charged for using the highway); (3) consulting
experts about the most suitable toll amount. Due to the difficulties in finding the real congestion
times in selected capitals, it is considered theoretically that the peak morning and evening
periods are the most congested times. Therefore, the highest toll amount was assigned to these
times. This made the proposed scheme easily understandable for the respondents. The
respondents were informed that half of the generated revenue from implementing the scheme
would be used for improving public transport, and the other half would be earmarked for the
enhancement of the road network.
Table 1. The Proposed Pricing Strategy
Time
Congestion Fee
Budapest
Amman
Tunis
Ulaanbaatar
Damascus
From 06:00 to 09:00
450 HUF a
1.0 JOD b
1.0 TND c
2000 MNT d
600 SYP e
From 09:00 to 15:00
300 HUF
0.5 JOD
0.5 TND
1000 MNT
300 SYP
From 15:00 to 18:00
450 HUF
1.0 JOD
1.0 TND
2000 MNT
600 SYP
From 18:00 to 21:00
300 HUF
0.5 JOD
0.5 TND
1000 MNT
300 SYP
From 21:00 to 06:00
Free of Charge
a,b,c,d,e prices according to
Google (July 18, 2019)
a 1 HUF =
0.003 Euro
b 1 JOD = 1.26
Euro
c 1 TND =
0.31 Euro
d 1 MNT =
0.0003 Euro
e 1 SYP =
0.0017 Euro
27
2.5 Results
In the following subsections, the results of the data analysis are presented, including
the descriptive analysis, factor analysis, bivariate Analysis, and multivariate analysis.
2.5.1 Descriptive Statistics
The contingency table illustrates the percent distribution of the background
characteristics “city profile” of each city (see Appendix Table 1). Most of the background
measurements differed significantly from one city to another. For example, a total of 60% of
all respondents own a car. This percentage differed significantly for different cities. The highest
percentages were noted in Ulaanbaatar and Amman (67.7% and 67.3%, respectively) and the
lowest percentages in Budapest, Tunis, and Damascus (49.6%, 55.8%, and 57%, respectively).
Consequently, people in Ulaanbaatar and Amman are more likely to use their cars than in other
cities (59.7% and 64.6%). On the other hand, the majority in Budapest (64.5%) use public
transportation more often. In all cities, people were less likely to use soft mobility (bike/foot)
means, especially in Amman, where less than 3% use it. The scheme awareness varied
significantly from one city to another. More than 60% of the people in Damascus reported that
they had prior knowledge of the scheme, while this percentage was around 50% in Budapest
and Ulaanbaatar and 47% in Tunis. Amman reported the least awareness percentage, with 38%.
Moreover, the mean of scheme acceptability was higher, whereas the RP awareness was higher.
Specifically, Damascus ranked first with an acceptability mean of 2.9, while Amman was the
last with a mean of 2.1.
2.5.2 Factor Analysis
The questionnaire consisted of 23 close-ended questions in three sections. The structure
of the questions in both Section 2 and Section 3 consisted of blocks of questions that measure
various concepts. There were different blocks of questions concerning: (1) the “Sensing of the
traffic-related problems”, “Travelling behavior and attitude”, and “Ascription of the
responsibility”; and (2) the proposed congestion charging scheme and “Awareness”,
“Effectiveness of the scheme”, “Social norms concerning pricing measures”, and the usage of
the revenues. Each block of questions retained two factors except acceptability, resulting in
only one factor. Table 2 displays the names of outcome factors and factor analysis measures.
According to the rule of thumb of Cronbach’s Alpha statistics, the reliability of composing the
outcome factors indicates a good internal consistency. Moreover, the KMO results for all
factors support the factor adequacy, with each factor explaining more than 50% of the total
variance.
28
Table 2. Factor Analysis: Outcome factors and their internal consistency
Factor
Number of
items
Cronbach's
Alpha
KMO
Total variance
explained
Sensing traffic Problems
.741
60.22%
Environmental
3
.70
Services
3
.61
Ascription of the responsibility
.74
55.30
Government
3
.73
Individuals
4
.61
Traveling norms & attitude
.70
54.42
Positive
6
.70
Negative
2
.84
Expected Mobility
.75
55.0
Positive
4
.75
Negative
3
.56
Scheme Effectiveness
.70
63.26
Positive
3
.79
Negative
3
.58
Acceptability
2
.70
.72
76.80
2.5.3 Bivariate Analysis: Hypothesis Testing
The bivariate analysis, specifically hypothesis testing, was used to examine the effect
of the city profile measurements on the pricing scheme acceptability factors. This technique
compares whether the means of acceptability significantly differ by the profile characteristics
within each city. The Independent Sample T-test was used if the profile measurement was
binary, while a one-way ANOVA test was used if the profile measurement contained more than
two categories (see Table 3).
Table 3. Mean of pricing scheme acceptability factor scores by background characteristics for cities
Background
characteristics/ City
Amman
Damascus
Tunis
Budapest
Ulaanbaatar
Gender
***
***
Male
-.508
-.079
-.030
.064
.256
Female
-.470
.439
.411
-.091
.132
Age
*
*
20–30
-.440
.120
.088
.017
-.008
31–40
-.465
.144
.308
.034
.430
41+
-.740
.179
.217
-.138
.253
Employment Status
*
*
Working
-.633
.115
.046
-.006
.147
Student
-.305
.170
.290
.002
.383
Other
-.314
.349
.468
-.229
.340
Income
Lowest
-.424
.222
.191
-.056
.348
Low
-.589
.268
.373
.004
-.305
Middle
-.543
.252
.226
.044
.053
Highest
-.329
-.262
-.056
-.098
.356
Mobility
*
*
**
29
Car
-.503
.039
-.110
-.328
.061
Public Transportation
-.550
.218
.381
.119
.493
Foot/Bike
.078
.313
.255
-.020
.237
Owning a car
***
**
Yes
-.561
.115
-.057
-.072
.072
No
-.378
.191
.456
.074
.459
Scheme Awareness
***
***
***
Yes
-.508
.358
.504
.084
.552
No
-.514
-.203
-.123
-.083
-.213
n
247
244
240
249
249
Significance level: * (p < 0.05); ** (p < 0.01); *** (p < 0.001).
As shown, the mean score of acceptability differs by the measurement profiles within
each city, e.g., females are more likely to accept the pricing scheme than males in all cities.
However, these differences are statistically significant only in the case of Damascus and Tunis
(0.439 and 0.411, respectively). Regarding age, the results differ significantly only in Amman
and Ulaanbaatar, with a slightly different pattern. People in Amman from all age groups do not
accept the pricing scheme, while in Ulaanbaatar, people aged between 31– 40 years old show
more acceptability (0.430). Similarly, the acceptability between the employment status groups
follows the variability pattern of age. People in Amman with different professions show
unacceptable attitudes towards the pricing scheme, while in Ulaanbaatar, they accept the
scheme irrespective of their occupations; however, with different percentages. Surprisingly, the
high-income level tends to have lower acceptability than other income levels in the three cities.
However, the acceptability does not significantly differ by income levels in any of the cities,
which is consistent with the result of previous research [92].
The effect of mobility attitude on scheme acceptability shows that people who use their
cars more often, in Damascus and Ulaanbaatar, are less likely to accept the pricing scheme
(0.039 and 0.061, respectively). In Tunis, people who use their cars do not accept the pricing
scheme (−0.110). At the same time, people who support soft mobility agree on the pricing
scheme (0.313, 0.237, and 0.255, respectively). Thus, people who do not own a car are more
likely to accept the scheme. Nevertheless, this is only significant in the case of Tunis and
Ulaanbaatar (0.465 and 0.459, respectively). Finally, prior knowledge of the pricing scheme
plays a vital role in acceptability. In general, the lack of knowledge (awareness) harms
acceptability mean. The results are statistically significant in Damascus, Tunis, and Ulaanbaatar
and indicate that people who have information about the scheme are more likely to accept it
(0.358, 0.504, and 0.552, respectively).
Surprisingly, in Budapest, none of the profile measurements affect the mean of pricing
acceptability significantly. Moreover, the mean acceptability scores for all measurements in
Amman are negative. The possible reason for this negative factor scores in the case of Amman
is that, at the time of distributing the survey, there was a massive protest in the whole country
against the austerity policy that applied measures like increasing the prices of goods and raising
the tax rate. Hence, people were reactive against any new policy imposing charges on the
citizens.
30
2.5.4 Multivariate Analysis: Regression Analysis
The effects of background profile measurements and some other predictors on the
pricing scheme acceptability were examined using the multiple linear regression approach. In
advance, the model’s assumptions were investigated for each city. The scatter plot between the
dependent variable (scheme acceptability) and each of the predictors indicated a linear
relationship between them, and the residuals are normally distributed. Moreover, the variance
inflation factor (VIF) for each predictor in the model was less than 3. In this analysis, I focused
on the factor predictors: sensing traffic problems, the effectiveness of the scheme, ascription of
responsibility, mobility attitude, owning a car, and awareness. The background measurements,
such as gender and owning a car, are included as binary and used as control variables in the
model (see Table 4).
Table 4. Multiple regression coefficients of the scheme pricing acceptability model for each city
Background
characteristics/ Cities
Amman
Damascus
Tunis
Budapest
Ulaanbaatar
Gender
Male
-.055
-.307*
-.231*
.166
-.039
Female1
--
--
--
--
--
Owning a car
Yes
-.068
.154
-.339**
.106
-.233
No2
--
--
--
--
--
Scheme Awareness
Yes
.078
.399**
.383**
-.021
.553***
No2
--
--
--
--
--
Sensing traffic problems
Environmental
.045
.057
-.092
.085
-.206**
Services
-.248*
.003
-.181*
-.085
.111**
Ascription of responsibility
Government
.053
.148
.071
-.066
-.064
Individuals
.193**
.194*
.127*
-.072
.025
Traveling norms & attitude
Positive
.024
.187*
.130
-.102
.107*
Negative
-.067
.134*
-.024
.077
-.054
Expected Mobility
Positive
.089
.154*
.235**
-.064
-.035
Negative
.023
-.119
.035
.006
.002
Scheme Effectiveness
Positive
.176*
.299***
.171*
.277***
.527***
Negative
.260***
.289***
.175**
.269***
.207***
R- Square Adjusted
.29
.51
.38
.25
.40
Significance level: * (p < 0.05); ** (p < 0.01); *** (p < 0.001).
1
Reference Category.
31
Consistent with the bivariate analysis, the model shows that the background
measurements (gender and owing a car) affect the acceptability in the same way in Damascus
and Tunis after controlling all other predictors. For example, males are significantly less likely
to accept the pricing scheme than females in these two cities (−0.307 and −0.231, respectively).
For owning a car, the results show that people who own cars are significantly less likely to
accept the pricing scheme than those who do not own cars only in the case of Tunis (−0.339).
Besides, the prior awareness about RP remains highly significant, while a lack of knowledge
negatively affects the scheme’s acceptability except in Amman and Budapest. The predictor
factors begin with sensing traffic problems: the environmental factor (i.e., respondents who
perceive the negative environmental impact of traffic-related problems as major or serious
problems) significantly affects the pricing scheme negatively only in the case of Ulaanbaatar
(−0.206). Although people reported that environmental issues due to traffic problems are crucial,
they are unwilling to accept the pricing scheme. Similarly, the effect of service factor, which
represents respondents who perceive the negative impact of traffic-related problems on service,
such as car crashes, as major or serious problems, is significantly negative in the case of Amman
and Tunis (−0.248 and −0.181), while it is positive in Ulaanbaatar (0.111).
According to the ascription of responsibility, surprisingly, the government
responsibility factor (i.e., respondents who consider the authorities responsible for the current
traffic-related problems) is not statistically significant in all cities. In contrast, the individual
responsibility factor has a significant direct impact on all the Arab cities. This indicates that if
people consider themselves responsible for solving the traffic problems, their acceptance of the
scheme increases significantly.
All traveling attitude factors have a significant direct effect on scheme acceptability in
Damascus, while only the positive attitude factor has the same effect in Ulaanbaatar. Moreover,
the expected individual mobility upon scheme implementation (i.e., the expected positive
mobility behavior where people are willing to reduce their use of cars) has a significant direct
effect on accepting the pricing scheme only in Damascus and Tunis (0.154 and 0.235,
respectively).
As anticipated, the scheme’s effectiveness factors are statistically significant in all cities.
There is a significant direct effect of the scheme’s effectiveness on the pricing acceptability
after controlling for all other variables. This indicates that people are willing to accept the
pricing scheme if they see it as an effective approach to reducing the adverse impact of
congestion.
In a nutshell, the model behaves differently in each city. Specifically, in Damascus, Tunis, and
Ulaanbaatar, the model explains about 51%, 38%, and 40% of the variation in scheme
acceptability, respectively. The model has the lowest ability to explain the variation of
acceptability in Amman and Budapest, amounting to 29% and 25%, respectively. In the last
case, most of the predictors in the model, except the scheme effectiveness, have no significant
effect on acceptability. Accordingly, the policy conservatism and culture of fear have a
significant impact on the scheme's acceptability.
2.6 Discussion and Conclusions
The literature shows that congestion charging is considered a successful strategy to
solve many traffic-related problems like pollution, noise annoyance caused by traffic, delay in
32
travel time, congestion and road accidents, etc. [113]–[115]. However, the empirical studies
showed that the public acceptability for implementing such a strategy is low [60]. Accordingly,
low acceptability is a pivotal obstacle to implementing congestion charging schemes, especially
in democratic societies. Many researchers examined the factors that affect the acceptability of
congestion charging schemes, including socio-economic characteristics or other variables that
affect travel behavior and the way to improve acceptability.
The current research shows that the congestion charging scheme acceptability differs
from one city to another and highlights the factors that profoundly affect the level of
acceptability. This implies that a RP scheme for any city or area should be uniquely conceived
for the best results. The bivariate analysis shows some fluctuations in the variables that
significantly affect the acceptability within each city. Surprisingly, none of the background
characteristics have a statistically significant effect on the scheme acceptability in the case of
Budapest, while only the age group and employment status significantly affect the acceptability
in Amman. At the same time, Damascus, Tunis, and Ulaanbaatar share some similarities in the
background characteristics that statistically affect the acceptability. This indicates that the
identity of each city and its general policy implications determine which factors are important
regarding the acceptability of a congestion charging scheme. However, the two factors of the
scheme’s effectiveness have a direct statistically significant effect in all cities.
The current study uses the regression model to examine the factors that affect the
acceptability of RP significantly within each investigated city. The model shows an irregular
pattern of the factors that statistically affect the acceptability scheme within each city. However,
it demonstrates that the effectiveness of the schemes is crucial and affects the acceptability
significantly, consistent with other researches [61], [67], [68]. The awareness of the RP factor
is not statistically significant in the case of Vienna, Athens, Como, Dresden, and Oslo. However,
in our study, it is statistically significant in the case of Damascus, Tunis, and Ulaanbaatar. The
current study emphasizes the significant direct effect between the scheme knowledge
(awareness) and the level of acceptability. Furthermore, it highlights the role of “travel behavior”
and “individual responsibility for traffic-related problems” on the RP acceptability.
In conclusion, the study uses a relatively small sample size of the studied populations.
Therefore, the results cannot be generalized to the country levels. However, it indicates the
factors which significantly affect the scheme's acceptability. To improve and enhance public
acceptability of the RP scheme, authorities should increase awareness about RP and clearly
explain the goal of implementing such a scheme and its positive effect on daily lives. Similarly,
people should feel responsible for causing traffic-related problems, which will lead to higher
acceptability of the proposed scheme. This can be achieved by including the concept of
externalities in the educational system and utilizing media to spread awareness about
individuals’ responsibilities for solving traffic-related problems. Accordingly, these strategies
will likely motivate people to change their travel habits and behavior to more environmental
friendly modes, especially if users are provided with suitable alternatives to passenger cars.
This will have a significant role in improving the RP acceptability.
The next chapter of this dissertation discusses the acceptability of RP under new travel
technologies (i.e., self-driving cars) by extending the used model here and including new factors
that are expected to affect both RP acceptability and self-driving car adoption.
33
Related publications to this chapter:
M. Shatanawi, F. Abdelkhalek, and F. Mészáros, “Urban Congestion Charging
Acceptability: An International Comparative Study,” Sustainability, vol. 12, no. 12, p. 15, 2020,
doi: https://doi.org/10.3390/su12125044.
M. Shatanawi, S. Boudhrioua, and F. Mészáros, “Comparing Road User Charging
Acceptability in the City of Tunis and Damascus,” MATEC Web Conf., vol. 296, p. 02002,
2019, https://doi.org/10.1051/matecconf/201929602002.
M. Shatanawi, M. S. Csete, and F. Mészáros, “Road User Charging: Adaptation to the
City of Amman,” University of Dunaújváros, Hungary, Nov. 2018, p. 10.
Thesis I
I found a significant positive relationship between the acceptability of road pricing and
the factors "scheme's effectiveness", "scheme's awareness", and "individual
responsibility for traffic-related problems". Moreover, the acceptability of road
pricing does not significantly differ by income level. Using surveys in five capitals, I
determined the factors and socio-demographic characteristics affecting road pricing
acceptability using dimension reduction techniques and regression analysis.
34
3 Chapter Three - The Interrelationship between Road Pricing
Acceptability and Self-Driving Vehicle Adoption: Insights from
Four Countries
3.1 Introduction
Advancement and expansion in the field of autonomous vehicles (AVs) and shared
autonomous vehicles (SAVs) are burgeoning quickly with the intense competition among
motor companies to capture market share, as evidenced by the Navigant Research
Leaderboard’s ten criteria (e.g., vision, technology, marketing, and others) for evaluating which
manufacturers are better positioned in the sector of automated driving [36], [37]. Considering
the increasing role of AVs and SAVs as future travel modes worldwide, increased legislation
is being reviewed and implemented in various countries and regions [5], [6]. In addition to AVs,
SAVs will also likely emerge as an on-demand travel service, being used as taxis or for
carsharing [26]. Consequently, growth in the AV and SAV sectors opens a wide field of
research and development both in industrial and academic contexts.
Since AVs are new travel modes with no human intervention [10], users of AVs can
utilize their travel time more effectively by executing other activities such as reading, working,
or even sleeping instead of driving [9], [10]. Moreover, AV sensors can assess the environment
and traffic conditions, thus providing comfort and safety for users [39]. AVs and SAVs are
expected to have substantial benefits, particularly in regard to improving energy consumption,
reducing environmental impacts, and increasing accessibility [40], [116]. However, the impact
of AVs and SAVs on road network congestion is unclear and may lead to serious problems [3],
[12].
On the one hand, the tighter headways between AVs and optimal utilization of
intersections will increase traffic throughput [8], [9]. On the other hand, AVs and SAVs will
most likely increase the number of trips and traveled miles on roads due to their improved
accessibility. In particular, their use by those who cannot drive because of age or disability will
increase the number of cars on the road and aggravate congestion [11], [116].
As mentioned in the previous chapter of this dissertation, road pricing (RP) is regarded
by many transport professionals, economists, and traffic engineers as a successful measure in
mitigating traffic-related problems such as congestion and reducing carbon emissions [14]–[16].
Moreover, RP is being adopted by several cities worldwide such as Singapore, Oslo, London,
Stockholm, and Milan [17]. Therefore, the use of RP as a travel demand management tool can
also play an important role in tackling the expected upsurge in congestion associated with the
emergence of AVs and SAVs. Despite the expected improvement of traffic-related problems
through the application of RP, there is also public resentment towards such schemes, as drivers
do not want to pay for the use of roads that were previously free [60], [62], [63]. Low public
acceptance towards the implementation of an RP scheme hinders its introduction [66]. For
example, authorities in Auckland, Copenhagen, Netherlands, and Edinburgh failed to
implement RP schemes due to public rejection [64], [84], [117]. However, case studies from
Stockholm and Milan showed that public acceptance of RP schemes could be enhanced if they
35
are properly introduced and include measures such as the use of revenues for improving public
transport (PuT) services [65], [80], [118].
Adopting new travel technologies such as AVs and SAVs and the successful
implementation of RP require initial public acceptance. Therefore, since the late 1980s,
numerous studies have investigated the acceptability of RP and generated a vast amount of
literature. Likewise, many researchers and consultancy companies have developed
questionnaires to investigate public perceptions of the advantages and disadvantages of AVs
and SAVs [43]. However, to the best of my knowledge, there is no study yet that has
investigated RP acceptability in connection with the adoption of AVs and SAVs and the factors
influencing them. Therefore, addressing this gap was the primary research aim of this study.
Considering that AVs and SAVs will likely be operating on the streets in the future, RP will be
a suitable measure to manage the travel demand, but it still requires public acceptance.
In this research, a questionnaire was developed and distributed to residents of Brazil,
Jordan, Ukraine, and Hungary. 657 valid responses were received. The questionnaire included
various latent variables derived from previous well-known models to explore public preferences
relating to RP, AVs, and SAVs. Analysis of the received data using different econometric
models provided insight into the public perception of RP, AVs, and SAVs. It also sheds light
on the relationship between the survey’s latent variables and public perception.
A review of previous research studies on the acceptability of RP and the adoption of
AVs and SAVs indicates that this study is distinctive with respect to simultaneously studying
the relationships among RP acceptability, preference for future cars, latent variables, and socio-
demographic characteristics. Therefore, this research opens new avenues for understanding the
factors influencing RP acceptability for addressing traffic congestion issues foreseen as a result
of the increased accessibility of self-driving cars and an increase in their presence due to their
various benefits. Here it is worth mentioning the recent study by Shatanawi et al. that discussed
RP adaptation to future cars, in which the author deployed a stated preference experiment
including different attributes (e.g., travel time and travel cost). However, the research results
were limited to the impact of socio-demographic characteristics on both RP acceptability and
future car choice [13]. Moreover, this research is an extension of the previous thesis, mentioned
in the second chapter of this dissertation [79].
The economic level of a given country has been shown to play a role in influencing the
adoption of automated vehicles through GDP per capita [119]. Therefore, the countries selected
in this research illustrate its breadth by analyzing the research impacts in countries from four
different regions: Hungary (Central Europe), Brazil (South America), Ukraine (Eastern Europe),
and Jordan (Middle East). These countries also represent different economic conditions: Jordan
and Brazil have developing economies, while Ukraine has an economy in transition, and
Hungary has a developed economy [120]. However, little research has been carried out in these
countries with reference to RP, AVs, and SAVs. In light of this, participants belonging to
different demographics, cultures, languages, and exposures (in terms of transportation systems,
economic conditions, environmental conditions, and other factors) are involved in this research,
highlighting its broad scope. Table 5 summarizes a few of the characteristics of the four
countries under study to provide an overview.
36
Table 5. Overview of the key characteristics of the countries under study
Criteria
Brazil
Jordan
Ukraine
Hungary
Area
8,5 Mkm2
0.089 Mkm2
0.603 Mkm2
0.093 Mkm2
Population and Density
210 Million,
25/km2
10 Million,
113/km2
41.7 Million,
69/km2
9.76 Million,
105/km2
GDP
$1.434 Trillion
$44.566 Billion
$153.895 Billion
$154.562 Billion
Vehicles in Use/1000 People
210.07^
123.38^
213.66^
377.52^
Passenger Vehicles Annual
Sales
1,752,328
14,000
88,437
131,885
Roadway Density (Km/100
Km2)
23
8
28
227
Rail Network Length (Km)
29817
622
19787
7945
^ (https://datahelpdesk.worldbank.org/knowledgebase/articles/906519#Upper_middle_income)
Thus, the contribution of this thesis to the literature on RP, AVs, and SAVs is twofold:
firstly, it investigates the relationship between RP acceptability, future car choice (AV or SAV),
and the studied latent variables. Secondly, it explores the cognitive determinants of RP
acceptability and future car choice.
This thesis is structured as follows: Section 2 provides a review of previous research
relevant to the adoption of AVs and SAVs. Section 3 presents the theoretical background of the
latent variables used to investigate the acceptability of RP, AVs, and SAVs. Section 4 elaborates
on the survey design and presents the survey instruments along with the analytical framework
of the research. The results of this study are provided in Section 5 and discussed in Section 6.
Finally, Section 7 highlights the conclusions of this research and provides insights into policy
implications as well as the limitations of the study.
3.2 Literature Review
Extensive research has been carried out on the acceptability of RP since the late eighties
(See Section 2 of Chapter 2), with comparatively fewer studies being conducted regarding
public concerns related to the adoption of AVs and SAVs. However, to the best of my
knowledge, there are as of yet no studies that interlink both RP acceptability and the adoption
of AVs and SAVs, including the impacts of various factors on both. This study aims to
contribute to bridging this research gap. The sections that follow summarize the main findings
of questionnaire studies examining public perspectives on the adoption of AVs and SAVs,
respectively.
3.2.1 Adoption of AVs
The boom in AV technology is evident, and numerous studies have estimated that AVs
will capture future market share. Payre et al. investigated the opinions of French travelers
regarding AVs. Out of 421 respondents, 78% were willing to buy an AV at a much higher price
than a regular car. The survey results revealed that the most preferred situations for using an
AV were monotonous driving situations such as highways or stressful driving conditions such
as congested areas [121]. Another international survey-based study investigating public opinion
on automated vehicles by Kyriakidis et al. estimated that automated vehicles will acquire a 50%
market share by the year 2050. However, higher prices of AVs will also generate negative
37
externalities, resulting in market stagnation if innovative measures are not implemented [122].
Nevertheless, pricing incentives (e.g., subsidies) are expected to have a significant role in
accelerating the market penetration of AVs. If government agencies subsidize AVs in the early
deployment and near-saturation stage, the market share of AVs can increase drastically [123].
Furthermore, the results of a survey investigating the network effects of connected and
individual AVs in South Korea indicated that consumers prefer AVs over conventional cars
(CC), and the network effects of AVs significantly affect consumer choice [124].
AV adoption in relation to individuals’ status was studied by Bansal et al. through an
online survey in Austin, Texas. The results showed that new technology lovers, those living in
urban areas, and those who have experienced road crashes are interested in adopting AVs [125].
On the other hand, the adoption of AVs in relation to attitudinal variables was investigated by
Leicht et al. through a survey that studied the relationship between consumer innovativeness
and intention to purchase AVs in France. The main findings showed that consumers’
performance expectancy, effort expectancy, and social influence, as defined by Venkatesh et al.
[126], are major drivers of AV purchase intention [127]. Another empirical study focused on
the adoption factors of AVs, which were necessary for millennials in smart cities, found that
the perceived benefits of AVs are vital for their adoption, and their perceived safety can
significantly affect concerns regarding their use. Moreover, personal and societal benefits were
the most influential factors for adopting AVs for people aged between 20 and 30 years [108].
AV's economic and other beneficial aspects (e.g., improved accessibility) are vital for
the adoption of future cars. AVs can facilitate the movement of elderly and disabled people
who were not previously able to drive [116], [128]. Fagnant and Kockelman stated that AV
technology is expected to alleviate congestion, lower parking demand, enhance fuel economy,
and drastically change the transportation systems in the U.S. [116]. Their research estimated
the annual economic benefits of using AVs at around $27 billion with only a 10% market share
and has an estimated potential of $450 billion in annual savings in the U.S. only. Rahimi et al.
explain that negating public concerns surrounding AVs and promoting their benefits in terms
of cost, time, and functionality will increase inclination towards AV adoption in the U.S. [129].
Previous studies highlighted the perceived risks associated with AV adoption, such as
system hacking and loss of data privacy. These public concerns about AVs undermine their
acceptability. For instance, public acceptance of AVs will decrease if they are programmed to
sacrifice their passengers in case of a crash to save the people on the road [130]. Research has
shown that the willingness of Americans to pay for the adoption of AVs will not drastically
increase if the associated policies are not introduced, and the prices of AVs are not rapidly
reduced [1]. Similar survey-based research has reported that Americans are more worried about
AVs with respect to system failures, data privacy, and interaction with human-driven vehicles
[131]. A survey in Dublin concluded that the majority of respondents were concerned about the
interaction of AVs with other road users and technical failure [132]. Bezai et al. examined 400
papers worldwide related to AVs, and an analysis of the shortlisted 140 papers concluded that
all the barriers affecting the acceptability of AVs could be separated into two categories:
user/government perspectives about AVs and information and communication technology of
AVs [133]. The diversification of the rapidly increasing research in the field of AVs is reviewed
by Sciaccaluga and Delponte, who recommend the use of new instruments such as gamification
to analyze users’ sensations, perceptions, and fears about using AVs in a new way [134].
38
Another barrier to the adoption of AV technology is cyber and information security threats
which were investigated by Maeng et al.; they found that consumers are highly sensitive about
their personal data privacy and communication failures in AVs [135].
AVs are expected to function as future long-distance travel modes. A study exploring
the implications of long-distance travel in Michigan after introducing AVs by LaMondia et al.
estimated that AVs would emerge as the preferred long-distance travel mode for less than 500
miles against private cars and airlines [136]. Other estimations of shifts in long-distance travel
modes using the rJourney model in the U.S. indicated that wide acceptance of AVs as a long-
distance travel mode would reduce U.S. domestic airline revenues by 53% and that the advent
of AVs would impact the destination choice of passengers [137]. Anticipation of how AVs and
SAVs will affect travel across the Texas megaregion showed that domestic air travel is expected
to fall by 82%, and congestion of roads is expected due to the 47% increase in vehicle miles
traveled by the year 2040 unless regulatory policies like RP are implemented [138].
3.2.2 Adoption of SAVs
The recent advances in the field of AVs, SAVs have been presented as a solution to
traffic-related problems. A study on the adoption of single-occupant SAVs in Singapore using
actual travel data found that each SAV can replace three privately owned vehicles [139].
Fagnant and Kockelman designed an agent-based model for SAVs in a grid-based urban area
to estimate the environmental benefits of adopting SAVs instead of conventional vehicles. The
study concluded that each SAV could replace up to 11 conventional private vehicles, while on
the other hand requiring 10% more travel distance [11]. A similar simulation in Austin, Texas,
revealed that each SAV could replace 9 CCs with an additional 8% traveled miles added to the
trip [140]. Another study by Chen et al. produced a model showing that electric SAVs can have
a similar per mile cost compared to CCs for low-mileage households. Each electric SAV can
replace 9 conventional private vehicles and remain competitive against conventional carsharing
services [141]. However, the last three mentioned studies did not consider the ride-sharing
option in the simulation of SAVs, as they considered the SAV to operate as a driverless taxi. A
study that accommodated ride-sharing options for a fully electric SAV fleet, as the adoption of
electric vehicles is growing worldwide [28], investigated the implications of SAVs on the
performance of the Budapest road network using simulation-based dynamic traffic assignment.
The results showed that increasing the SAVs share would improve the overall network
performance [27]. An analysis of the potential benefits of dynamic ride sharing using SAVs
over traditional taxis in New York City demonstrated that the fleet size could be reduced by
59% without increasing the waiting time. A reduction of carbon emissions of up to 866 metric
tonnes per day was also reported in the same study [142].
Similarly to AVs, the acceptability of SAVs is connected to individual’s status and
attitudinal variables. Results of a stated choice survey indicate that young travelers are more
attracted to the adoption of SAVs with dynamic ride sharing [26]. Similar results regarding
young people's willingness to adopt new transportation technologies were found by Tian et al.,
who added that consumers’ main concerns about SAVs as an alternative to car-sharing options
are their cost, access time, and availability [143]. Merfeld et al. conducted a Delphi study on
drivers, barriers, and future developments of car sharing with SAVs in the next ten years and
found that a strong perception of technological aspects, consumer acceptance considerations,
and legislative concerns are the most important factors in the adoption of SAVs [144]. In Italy,
39
the inhabitant of Naples showed resistance to using future cars, as they are willing to pay more
or spend additional travel time in traditional transport modes rather than using new traffic
technologies for the same trip. The reason behind this reluctance is related to concerns over
security and safety. However, the same research indicated that males and young bus or taxi
users are less reluctant to use SAVs as driverless taxis than female and older users (above 40
years old), respectively [145].
SAVs with dynamic ride sharing imply that passengers would travel with strangers in
the same vehicles for a certain period of time and also entail an increase in travel time due to
the loading and unloading of other passengers. Lavieri & Bhat examined these issues and found
that trip purpose determines the anxiety towards these concerns. Travelers on a leisure trip are
more sensitive to riding with strangers but are less sensitive to extra travel time and vice versa
on a commute trip [146]. Nevertheless, various studies predict that AVs will probably function
as pooled AVs. For example, a choice experiment showed that 61% of Swiss users opted for
pooled AVs rather than private AVs [147]. A study on the willingness to pay for SAVs
accommodating dynamic ride-sharing with strangers used 70 questions in a stated preference
survey and was answered by 2588 respondents; it suggested that willingness to pay for ride-
sharing will increase over time. The study also suggested that SAVs will be preferred for long-
distance business travel [148].
3.3 Theoretical Background
The section is connected and continuation of Section 2.3 in Chapter 2, where all
mentioned latent variables in Section 2.3 are used here, plus some other latent variables that
may influence the acceptability of RP and the adoption of AVs and SAVs. As mentioned earlier,
these latent variables were drawn from a previously developed heuristic model [61], [66], [94],
[95] and are based on the theory of reasoned action and planned behavior [96], [97]. I mention
in this section the newly added latent variables as the other can be found in Section 2.3.
• Safety and Security
A number of studies have explored public opinion concerning new travel technologies
and have shown that respondents regard safety as the paramount advantage and essential factor
for adopting AVs and SAVs [125], [149]. Some of the studies revealed concerns about the
safety of AVs, such as a vehicle’s computer system being hacked or a vehicle’s system failure
[131], [150], [151]. Other issues include legal liability, traveler privacy, and interactions with
CC.
• Equity
One of the main reasons for the low acceptability of RP schemes is that people consider
them to be unfair. Hence, perceived equity is one of the essential requirements for a scheme's
acceptability [152]. The “intrapersonal” component of perceived equity concerns the
respondents' personal cost-benefit ratio before and after applying the policy measure [66]. In
our study, equity refers to perceived intrapersonal equity, and this component is considered for
analysis.
• Fairness
Similar to equity, perceiving an RP scheme as fair is also a prerequisite for its successful
implementation. This can be achieved by the appropriate utilization of expected revenues,
which is proven to be an essential factor for the acceptability of RP schemes [78], [152]. The
40
fairness of an RP scheme can be assessed through perceived optimal revenue usage, the level
of trust in their government, and the perception of other elements of fairness (e.g., RP should
be implemented for all vehicles without exemptions; RP should vary according to the
congestion level).
3.4 In-depth Analyses
This section describes the methods used in the study. The survey design will begin with
a description of the valid response rate among the four countries of interest. This will be
followed by an explanation of the survey instruments and analytical framework to provide a
holistic picture of the study goals. Multiple statistical techniques were used in the study (i.e.,
factor analysis, multiple linear regression (MLR), and multinomial logit model (MNL)). With
the help of these techniques, a set of models were constructed. The purpose of the models is to
answer the following research questions:
1. What factors play a role in affecting RP acceptability in the four countries of interest?
Do socio-demographic characteristics (e.g., age and income) influence the RP
acceptability in the four countries of interest?
2. What factors play a role in affecting the adoption of AVs and SAVs in the four countries
of interest?
3. Do the added variables (e.g., AV_Perceived_Ease_of_Use, and
AV_Safety_Security_Concerns) significantly affect RP acceptability or the adoption of
AVs and SAVs in the four countries of interest? How do the additional variables impact
the acceptability of RP and willingness to adopt AVs and SAVs in the four countries of
interest?
4. To what extent do the respondents from the four countries of interest perceive their
governments as trustworthy in collecting RP tolls? In which areas do the respondents
from the four countries of interest expect their government to spend the collected road
toll?
3.4.1 Survey Design
An online survey was distributed simultaneously in Brazil and Jordan during January
and February of 2020 and in Hungary and Ukraine during March and April of 2020. Qualtrics
survey software was used to create the survey. Explanatory videos about RP, AVs, and SAVs
were integrated into the survey to provide respondents with a better understanding of these
concepts. These videos conveyed the information in the native languages of the respondents,
which allowed a better understanding of the survey. In this way, a large amount of textual
information was summarized rather than being included in the survey. The videos introduced
respondents to AVs, SAVs, and RP. It was highlighted that AVs are driverless cars that do not
require human intervention while driving; therefore, travel time can be used to engage in
different activities such as reading, sleeping, working, and relaxing instead of driving. SAVs
are treated as driverless shared taxis, which can be requested through a smartphone. This
implies that other passengers having a similar destination may share the SAV, which results in
lower cost, less privacy, a lower level of comfort, and longer waiting time. Information
presented in the video regarding SAVs was drawn from Lokhandwala and Cai [142]. At the
end of the video, the concept of RP was explained. As the responses have the potential to be
41
significantly affected by how the introduced concepts are presented, the video was presented in
a neutral manner, with no evaluative adjectives used to avoid the problem of induced bias and
endogeneity. In addition, backward translation was carried out.
The survey was distributed randomly using social media platforms such as Facebook.
Consequently, self-selection sampling was adopted, as the participants made the decision either
to participate or not in the survey. Out of 1,999 initial respondents to the survey, only 723
respondents completed it. Only the respondents who answered the questionnaire in more than
10 minutes and were declared as "A normal response" by Qualtrics were considered. The final
number of total usable responses was 657, as detailed in Table 6. The valid response rate in this
research ranged between 24% and 43% and fell within the range of response rates (20% and
54%) mentioned in [153], [154], indicating that the distribution was wide and unbiased.
Table 6. Distribution of Responses Across Countries
Brazil
Jordan
Ukraine
Hungary
Total
Survey Initiations
598
885
315
201
1,999
Survey Completions
269
270
100
84
723
Survey Completions in
more than 10 minutes
255
248
77
77
657
Valid Response Rate
43%
28%
24%
38%
33%
The questionnaire consisted of three main parts. First, questions about the behavioral
response towards the latent variables which may influence RP acceptability and the adoption
of AVs and SAVs were presented. Secondly, questions related to RP acceptability, future car
choice, and the utilization of revenues were introduced (e.g., the respondents were asked to
assess their level of acceptance of the application of RP in their cities based on a 5-point Likert
scale from totally unacceptable (1) to totally acceptable (5)). Finally, the socio-demographic
characteristics of respondents were collected.
3.4.2 Survey Instrument
This section presents the derivation of the factors from the original survey questions.
Building a model utilizing raw survey questions may be challenging and can also result in a
difficult interpretation of the results. Therefore, to overcome this issue, factor analysis is often
applied. For example, the previously explained variable “perceived effectiveness” in the
theoretical background section resulted in two factors that were derived from nine items, as
shown in Table 7. The items utilized to develop each of the investigated factors are presented
in Appendix Table 3.
42
Table 7. Factor analysis example
#
Item Description
Extracted Factor
1
I think the application of road pricing is likely to reduce travel time.
Perceived_Usefulness_RP
2
I think the application of road pricing is likely to decrease the congestion
level.
3
I think the application of road pricing is likely to reduce air pollution.
4
I think the application of road pricing is likely to reduce noise, annoyance,
and disturbance.
5
I think the application of road pricing is likely to result in a better fuel
economy.
6
I think the application of road pricing is likely to reduce the number of
accidents and incidents.
7
I think the application of road pricing is likely to increase the price of the
trip.
Negative_Expectations_RP
8
I think the application of road pricing is likely to make the PuT modes
more crowded.
9
I think the application of road pricing is likely to result in increasing social
inequality among the citizens.
3.4.3 Analytical Methods
A two-step approach was applied in the data analysis. First, factor analysis was
conducted to reduce the large set of items into a lower number of factors. Principal components
analysis was used as the extraction method with varimax rotation. Cronbach’s Alpha results
indicated a sufficient level of internal consistency, with each factor explaining more than 50%
of its total variance, except “AV_Safety_Security_Concerns” in the Jordan sample. All the
extracted factors achieved satisfying results; a sample of the factor analysis results is shown in
Table 8.
Table 8. Descriptive values of some factors generated by factor analysis
Country
Variable Name
Number of
items
Alpha Cronbach
Total variance
explained
Brazil
Perceived_Usefulness_RP
6
0.82
52.99
Sensing_Traffic_Problems
6
0.80
50.45
Social_Norm
2
0.72
78.26
Jordan
Perceived_Usefulness_RP
6
0.84
56.02
AV_Safety_Security_Concerns
7
0.79
44.95
Social_Norm
2
0.77
81.16
Hungary
Perceived_Usefulness_RP
5
0.85
63.47
Negative_Expectations_RP
3
0.75
66.51
Willingness_to_share
4
0.75
57.52
Ukraine
AV_Awareness
3
0.85
77.61
Social_Norm
2
0.83
85.43
Perceived_Usefulness_RP
5
0.82
58.74
With the use of a dimension reduction technique (i.e., principal components analysis),
the large initial set of items was reduced to a reasonable number of factors. Next, the selected
factors with socio-demographic characteristics were used to understand RP acceptability
43
through the use of MLR, while the AV and SAV adoption preferences were modeled with MNL.
This is further depicted in Figure 1.
Figure 1. Analytical Framework
3.4.4 Descriptive Statistics
Appendix Table 2 shows the distribution of the socio-demographic characteristics in the
form of “country profiles” for the four countries. A total of 657 valid responses were collected
from four countries, with Brazil having the highest share with about 39% of the total collected
sample and Jordan having the second highest share with about 38%, while Hungary and Ukraine
accounted for about 23% collectively. The age distribution of the respondents is relatively
young, with all four countries having more than 70% of their respondents falling below the age
of 38. The gender distribution was relatively balanced across the four selected countries. In
each country, the higher income group was shown to be the smallest out of the three income
levels. The majority of the respondents reported to be educated, and those with a bachelor's or
postgraduate degree (Ph.D. or Master) showed the highest number of responses compared with
other educational levels. Similarly, regarding their employment status, the respondents reported
being full-time workers, and again this mirrors the age distribution of the respondents. The
results concerning driving licenses and car ownership illustrate the status of PuT in each of the
four countries. Respondents from Brazil and Jordan report higher rates of having a driving
license or owning a car compared to respondents from Hungary and Ukraine. This finding might
reflect that the possibility of owning a vehicle increases in countries with lower quality of PuT.
The use of revenue is one of the most crucial factors that determines the public
acceptance of RP [72], [89]. An item was included in the questionnaire containing seven
suggested approaches for using the revenue from RP. The answers are based on a Likert scale
from 1 to 5 representing “Totally disagree” to “Totally agree”; the responses to this item can
be seen in Figure 2. The respondents were shown to favor approaches pertaining to soft mobility,
reducing vehicle taxes, reducing PuT and vehicle customs fees, improving PuT, and
constructing new roads, with the exception being the approach that RP revenues should be used
to support the state’s budget, where the average responses showed the lowest mean value.
44
Figure 2 summarizes the results from the four countries together, showing that respondents
from all four countries of interest showed a similar pattern in answering this question.
Figure 2. Mean Values of respondent's preferences for using RP revenues
The reluctance of the respondents to use RP revenues to support the state budget can be
inferred from Figure 3. The respondents were asked to state whether or not they trust the
government using the revenues of RP. Figure 3 shows that most respondents in the four
countries do not trust their governments to responsibly invest the collected revenues. Findings
reflect the respondents' lack of confidence in their respective governments in the four countries.
Figure 3. Respondent's trust in their governments regarding the use of RP revenues
2.79
3.72
4.17
3.55
3.91
3.48
4.03
To support state's budget
To construct new roads
To enhance public transport
To lessen taxes for users
To lessen public transport fares
To lessen vehicles' customs fees
To enhance the soft mobility
Potential use of RP revenues
Mean
Disagree
Neutral Agree Strongly Agree
Strongly Disagree
93%
80% 81%
88%
7%
20% 19%
12%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Brazil Jordan Hungary Ukraine
No Yes
45
3.5 Results
This section presents the effects of the investigated factors on RP acceptability using
MLR. In addition, the results of AV and SAV adoption using MNL models are analyzed to
understand individual preferences toward AVs and SAVs based on the investigated factors.
3.5.1 RP Acceptability
The effects of the investigated factors and sociodemographic characteristics on RP
acceptability using MLR in Hungary, Jordan, Ukraine, and Brazil are presented in Table 9,
which includes the factors’ estimated parameters, level of significance, and model fit. The
sociodemographic characteristics are income and age. Overall, 20 parameters for each country
were estimated for the RP acceptability model. The intercept significantly differs from zero in
four models. The Hungarian model intercept is positive, indicating that Hungarian respondents
would accept applying RP regardless of the effects of other factors, while a negative tendency
is associated with respondents from other countries. While many of the estimated parameters
vary across the countries, some of them were found to share a common sign.
Unsurprisingly, the “Environmental_Oriented_Users” factor has a significant positive
effect on RP acceptability. This indicates that respondents who consider their environmental
impact while planning their trips (e.g., using less polluting vehicles) are more likely to accept
the implementation of RP. Similarly, in all models, “Social_Norm” is statistically significant
and has a remarkable positive effect on RP acceptability; this implies that the respondents can
be positively influenced by their family and friends to accept the application of RP. The
explained variance differed across countries of interest. Models of Hungary and Ukraine
explained about 54.6% and 54.9% of the variation in RP acceptability, respectively, whereas
the Brazilian model explained 31.4%. The least explanatory model was Jordan, with only 16.4%.
Table 9. MLR Parameters of RP acceptability
Variable
Hungary
Jordan
Ukraine
Brazil
Intercept
0.16*
-0.47***
-0.23*
-0.34***
RP_Awareness
0.1***
0.032
-0.26***
-0.17***
AV_Awareness
0.08**
-0.05**
-0.050
-0.06***
PuT_Users
-0.16***
-0.030
-0.15***
0.010
Enjoy_Driving
-0.19***
0.04*
-0.15***
0.07***
Cycling_Users
0.040
-0.030
0.13***
0.11***
Walkers
-0.12***
0.030
-0.020
-0.010
Technology_Interest
0.06*
0.040
0.47***
-0.04*
Environmental_Oriented_Users
0.17***
0.17***
0.11***
0.11***
Cost_Oriented_Users
0.060
0.030
0.08**
-0.04*
Sensing_Traffic_Problems
0.19***
0.05**
-0.050
0.2***
Negative_Expectations_RP
0.2***
0.05*
0.16***
0.11***
Willingness_to_Share
0.41***
0.19***
-0.2***
0.11***
RP_Perceived_Anxiety
-0.14***
0.020
0.14***
0.030
AV_Perceived_Ease_of_Use
0.1***
0.003
0.000
0.010
AV_Safety_Security_Concerns
0.16***
0.1***
-0.050
0.07***
Social_Norm
0.38***
0.16***
0.21***
0.31***
Fairness
-0.010
-0.06***
-0.08**
0.11***
Equity
-0.07**
-0.020
0.23***
0.17***
Income
0.004
0.001
0.0002*
-0.001
Age
-0.004
0.01***
0.010
0.01***
R-Square Adjusted
.546
.164
.549
.314
Significance Level: * (0.1 ≥ P > 0.05); ** (0.05 ≥ P > 0.01); *** (P ≤ 0.01)
46
3.5.2 AV and SAV Adoption
The estimated parameters of the MNL models are presented in this section. Two models
were generated for each country to assess the respondents’ behavior regarding the adoption of
AVs and SAVs. In the first model, “Model 1”, vehicle choice is a function of 19 factors derived
from the original survey’s questions using factor analysis. The second model, “Model 2”, was
generated to determine the impact of the inclusion of sociodemographic characteristics and
driving habit variables among the investigated factors, including “Age”, “Income”, “Gender”,
“Education”, “Employment”, “Driving license”, “Car ownership”, “Access to car as driver”,
and “Access to car as passenger”.
Table 10 to Table 13 display the parameters of the two models for each country
separately. The parameters of AVs and SAVs are relative to the reference mode CC. The models
representing each country's data are determined based on statistical tests like the Akaike
information criterion (AIC), Bayesian information criterion (BIC), and McFadden R2.
The alternative specific constant (ASC) represents the mean of all unobserved resources
of the utility. It could be noticed that ASCAV and ASCSAV are significantly different from zero
in the MNL models. Based on the Hungarian responses, both constants have a positive sign;
however, there is a preference for choosing AV over the other two modes. Regarding the
Jordanian model, both ASCAV and ASCSAV are significant and positively affect the utility
functions of these modes. Unlike Hungarian respondents, Jordanian respondents prefer SAVs
over AVs and CCs. In terms of the Ukrainian responses, ASCSAV is associated with a negative
sign, which increases the disutility of the SAV mode. Thus, based on the Ukrainian respondents’
viewpoint, CCs are still the preferred mode compared to SAVs. The responses from the
Brazilian model show that ASCSAV is significant and positively affects the utility function, and
SAVs are considered the most preferred mode.
47
Table 10. MNL of vehicle adoption in Hungary
Variable
AV Vs. CC
SAV Vs. CC
Model 1
Model 2
Model 1
Model 2
ASC (Intercept)
3.81***
1.79
1.24***
2.5
Awareness
AV_Awareness
1.49***
4.57***
2.12***
5.15***
RP_Awareness
-0.11
0.11
-0.26
-0.01
Travel Behavior and Attitudes
PuT_Users
0.16
0.42
0.10
-0.29
Enjoy_Driving
-0.5*
-1.57***
0.27
-0.44
Cycling_Users
-0.45
-0.91**
-0.62*
-0.98*
Walkers
0.5*
0.73
0.22
0.55
Technology_Interest
-0.12
-0.38
-0.78**
-1.42**
Environmental_Oriented_Users
0.48
1.98**
0.57
2.28**
Cost_Oriented_Users
-0.77**
-2.37***
-0.37
-2.04**
Sensing Traffic Problems
Sensing_Traffic_Problems
-0.66*
-0.52
-0.84**
-0.80
Perceived Effectiveness
Perceived_Usefulness_RP
-0.88**
-1.49**
-1.03**
-1.69***
Negative_Expectations_RP
-0.47
-2.0***
-0.54
-2.09**
Personal Effectiveness
Willingness_to_Share
0.69*
1.25*
0.6
0.61
RP_Perceived_Anxiety
0.35
3.22***
0.36
3.33***
Safety and Security
AV_Safety_Security_Concerns
0.06
-0.80
0.30
-0.52
AV_Perceived_Ease_of_Use
-0.40
-0.50
-0.27
-0.20
Social norms concerning RP
acceptability
Social_Norm
-0.34
-1.8***
-0.23
-1.44**
Fairness
Fairness
0.55**
1.38**
0.54*
1.1*
Equity
Equity
0.27
2.14**
0.03
1.98*
Age
0.002
-0.07
Income (ref: Low)
Medium income
3.83**
2.87
High income
0.15
-1.07
Gender (ref: Female)
Male
-0.12
1
Education (ref: less than bachelor)
Bachelor
3.67**
1.44
Postgraduate studies (Ph.D. or Master)
3.45**
2.77*
Employment (ref: Working)
Not Working
4.3**
3.35*
Driving license (ref: Yes)
No
1.39
0.83
Car ownership (ref: Yes)
No
-2.98
-3.44**
Access to car as driver (ref: Yes)
No
0..68
1.77
Access to car as passenger (ref: Yes)
No
3.07*
2.03
AIC
361.5
409.5
361.5
409.5
BIC
533
683.9
533
683.9
McFadden R2
0.265
0.34
0.265
0.34
Significance Level: * (0.1 ≥ P > 0.05); ** (0.05 ≥ P > 0.01); *** (P ≤ 0.01)
48
Table 11. MNL of vehicle adoption in Jordan
Variable
AV Vs. CC
SAV Vs. CC
Model 1
Model 2
Model 1
Model 2
ASC (Intercept)
0.15*
-0.14
0.95***
1.83***
Awareness
AV_Awareness
0.12*
0.14*
0.3***
0.44***
RP_Awareness
Travel Behavior and Attitudes
PuT_Users
-0.28***
-0.28***
-0.26***
-0.17**
Enjoy_Driving
0.02
0.03
0.11
0.13*
Cycling_Users
-0.01
-0.01
-0.10
-0.09
Walkers
0.14*
0.13
0.03
0.04
Technology_Interest
0.34***
0.36***
0.17**
0.09
Environmental_Oriented_Users
0.14*
0.17*
0.23***
0.27***
Cost_Oriented_Users
0.32***
0.29***
0.63***
0.61***
Sensing Traffic Problems
Sensing_Traffic_Problems
0.34***
0.35***
0.35***
0.31***
Perceived Effectiveness
Perceived_Usefulness_RP
0.49***
0.45***
0.06
0.08
Negative_Expectations_RP
0.02
-0.002
0.17**
0.16*
Personal Effectiveness
Willingness_to_Share
0.25***
0.26***
0.54***
0.6***
RP_Perceived_Anxiety
0.16*
0.17*
0.29***
0.33***
Safety and Security
AV_Safety_Security_Concerns
-0.3***
-0.26***
-0.22***
-0.16**
AV_Perceived_Ease_of_Use
-0.03
-0.03
-0.09
-0.08
Social norms concerning RP
acceptability
Social_Norm
-0.01
-0.03
0.13
0.19**
Fairness
Fairness
-0.12
-0.13
-0.01
0.02
Equity
Equity
-0.01
-0.01
0.14*
0.13
Age
0.01
-0.02**
Income (ref: Low)
Medium income
0.29
-0.18
High income
-0.01
0.05
Gender (ref: Female)
Male
-0.56***
-0.46***
Education (ref: less than bachelor)
Bachelor
0.01
0.16
Postgraduate studies (Ph.D. or Master)
0.3*
0.46***
Employment (ref: Working)
Not Working
0.03
-0.03
Driving license (ref: Yes)
No
0.37
-0.04
Car ownership (ref: Yes)
No
0.4*
-0.25
Use the vehicle as driver (ref: Yes)
No
-0.51***
0.32*
Use the vehicle as passenger (ref: Yes)
No
-0.05
-0.05
AIC
2206.5
2380.3
2206.5
2380.3
BIC
2406.0
2707.8
2406.0
2707.8
McFadden R2
0.13
0.16
0.13
0.16
Significance Level: * (0.1 ≥ P > 0.05); ** (0.05 ≥ P > 0.01); *** (P ≤ 0.01)
49
Table 12. MNL of vehicle adoption in Ukraine
Variable
AV Vs. CC
SAV Vs. CC
Model 1
Model 2
Model 1
Model 2
ASC (Intercept)
0.10
-3.05***
-0.42***
0.06
Awareness
AV_Awareness
0.14
-0.37**
-0.37**
-0.54**
RP_Awareness
0.23
0.54**
0.32**
0.37
Travel Behavior and Attitudes
PuT_Users
0.62***
0.87***
-0.09
-0.12
Enjoy_Driving
-0.45***
-1***
0.18
-0.13
Cycling_Users
0.28*
0.33*
0.07
0.07
Walkers
0.10
0.37**
0.25
0.45**
Technology_Interest
-0.20
-0.21
-0.24
-0.27
Environmental_Oriented_Users
-0.25*
-0.07
-0.21
-0.12
Cost_Oriented_Users
0.33**
0.23
0.33**
0.31
Sensing Traffic Problems
Sensing_Traffic_Problems
-0.47***
-0.8***
0.07
0..04
Perceived Effectiveness
Perceived_Usefulness_RP
-0.42***
-0.58**
-0.6***
-0.88***
Negative_Expectations_RP
0.37**
0.35*
0.48***
0.6***
Personal Effectiveness
Willingness_to_Share
0.4**
0.08
0.35**
0.55***
RP_Perceived_Anxiety
0.35***
0.25
0.64**
0.29
Safety and Security
AV_Safety_Security_Concerns
0.06
-0.3*
-0.28**
-0.12
AV_Perceived_Ease_of_Use
-0.3**
-0.24
-0.23
-0.32*
Social norms concerning RP
acceptability
Social_Norm
0.63***
0.51**
0.51***
0.53**
Fairness
Fairness
-0.14
0.15
-0.002
0.14
Equity
Equity
0.44***
0.41**
-0.13
-0.11
Age
0.05*
-0.03
Income (ref: Low)
Medium income
0.22
0.59
High income
-0.40
-0.92**
Gender (ref: Female)
Male
2.2***
0.68
Education (ref: Less than bachelor)
Bachelor
-0.29
0.24
Postgraduate studies (Ph.D. or
Master)
0.09
0.30
Employment (ref: Working)
Not Working
0.57
-0.30
Driving license (ref: Yes)
No
2.13***
-0.28
AIC
633
707.7
633
707.7
BIC
804.5
952.8
804.5
952.8
McFadden R2
0.18
0.27
0.18
0.27
Significance Level: * (0.1 ≥ P > 0.05); ** (0.05 ≥ P > 0.01); *** (P ≤ 0.01)
50
Table 13. MNL of vehicle adoption in Brazil
Variable
AV Vs. CC
SAV Vs. CC
Model 1
Model 2
Model 1
Model 2
ASC (Intercept)
0.15
0.3
1.93***
2.96***
Awareness
AV_Awareness
-0.21**
-0.13*
0.14*
0.16*
RP_Awareness
0.19*
0.12
0.03
-0.08
Travel Behavior and Attitudes
PuT_Users
-0.05
-0.09
-0.06
-0.005
Enjoy_Driving
-0.08
-0.22*
0.12
-0.09
Cycling_Users
0.05
-0.01
0.24***
0.09
Walkers
-0.15
-0.09
-0.14*
0.06
Technology interest
0.48***
0.51***
0.31***
0.29***
Environmental_Oriented_Users
0.23**
0.26**
0.26***
0.24**
Cost_Oriented_Users
0.1
0.04
-0.002
-0.1
Sensing Traffic Problems
Sensing_Traffic_Problems
-0.19*
-0.21**
0.1
0.004
Perceived Effectiveness
Perceived_Usefulness_RP
0.16
0.17
-0.22**
-0.16
Negative_Expectations_RP
-0.05
0.05
-0.15*
-0.13
Personal Effectiveness
Willingness_to_Share
0.17
0.18
0.55***
0.59***
RP_Perceived_Anxiety
-0.14
-0.23**
-0.14
-0.17*
Safety and Security
AV_Safety_Security_Concerns
0.03
-0.01
0.02
-0.05
AV_Perceived_Ease_of_Use
-0.09
0.01
0.02
0.12
Social norms concerning RP
acceptability
Social_Norm
0.03
-0.07
-0.2*
-0.27**
Fairness
Fairness
-0.16
-0.2*
0.08
0.03
Equity
Equity
0.23**
0.31***
0.18**
0.29***
Age
-0.02***
-0.06***
Income (ref: Low)
Medium income
0.43
0.09
High income
0.85***
0.91***
Gender (ref: Female)
Male
0.13
0.05
Education (ref: Less than Bachelor)
Bachelor
0.35
-0.001
Postgraduate studies (Ph.D. or
Master)
0.13
0.52**
Employment (ref: Working)
Not Working
-0.07
0.3
Driving license (ref: Yes)
No
0.71**
0.49
Car owning (ref: Yes)
No
-0.24
-0.16
Use the vehicle as driver (ref: Yes)
No
0.52*
0.38
Use the vehicle as passenger (ref:
Yes)
No
-0.14
-0.29
AIC
1747.92
1815.08
1747.92
1815.08
BIC
1967.41
2166.26
1967.41
2166.26
McFadden R2
0.10
0.15
0.10
0.15
Significance Level: * (0.1 ≥ P > 0.05); ** (0.05 ≥ P > 0.01); *** (P ≤ 0.01)
51
3.6 Discussion
This section provides a discussion of the presented results from the previous section.
The discussion is divided into three sub-sections. The first two sections discuss the MLR and
MNL, respectively. The third sub-section provides a summary of the results.
3.6.1 RP Acceptability
Road network congestion and its related issues are considered major problems in the
transport sector. Therefore, regulatory traffic policies are needed to control travel demand and
alleviate congestion. RP schemes are regarded as an effective solution to tackle such traffic-
related problems; however, authorities are presented with the challenge of trying to implement
RP schemes in an effective way that, at the same time, will not be rejected by the public. For
example, Selmoune et al. reviewed eight cases of RP scheme implementation that were either
accepted or rejected. This and other studies highlighted the difficulty in applying RP schemes
without strong political and public support and acceptability [155], [156]. Recently many
studies have investigated the public acceptability of applying RP schemes in various countries
[107], [157]. In their studies, Gu et al. and Noordegraaf et al. stated that only four main factors
of RP were used in most previous studies, namely equity, complexity, privacy, and uncertainty
[83], [158]. The current thesis expanded on these with the inclusion of other factors related to
RP implementation as well as the adoption of AVs and SAVs to investigate the effect of these
factors on RP acceptability in the era of AVs and SAVs.
The current thesis shows that the examined factors are significant and have a direct
effect on RP acceptability. Moreover, income has a statistically significant effect on RP
adoption only for Ukrainian respondents, while in the case of Jordan and Brazil, respondents'
age was shown to have a statistically significant effect on the acceptability of RP. The results
differ in different national contexts such that there are some fluctuations in the parameters that
significantly affect the acceptability of RP in each country. It can be inferred from Table 9 that
Hungarian respondents prefer applying RP in their cities. Although these models show
irregularity in the parameters across the investigated countries, the
“Environmental_Oriented_Users”, “Negative_Expectations_RP”, and “Social_Norm”
variables are statistically significant and positively affect RP acceptability in all countries. This
study showed that there is a significant and direct effect of the AV-related variables on RP
acceptability except in the case of “AV_Perceived_Ease_of_Use”, which is statistically
significant only in the case of Hungary. Consequently, only the Hungarian respondents who
expect that using AVs will be easy are more likely to accept RP schemes.
3.6.2 AV and SAV Adoption
This section discusses the effects of the investigated factors, travel behavior and
attitudes, and sociodemographic characteristics on preferred transport mode choice (AV, SAV,
CC). It is inferred from the results that most of the factors considered have a major role in
determining preferences toward choosing and adopting AVs and SAVs. Particularly,
“AV_Awareness” was associated with a positive attitude toward AVs and SAVs, which shows
that respondents who have more knowledge of new technologies are more likely to prefer AVs
and SAVs relative to CCs. An exception can be seen in the Ukrainian and Brazilian models,
which demonstrate the opposite perspective toward the adoption of both AVs and SAVs. The
52
results showed that respondents who enjoy driving are less likely to choose AVs and SAVs,
which is compatible with previous research findings [159], [160]. Not surprisingly, the
“Environmental_Oriented_Users” factor was positively significant in all countries except
Ukraine, so respondents with high sensitivity towards environmental issues have a high
propensity to use AVs and SAVs [159]. In all models, respondents willing to share their trips
with others are more likely to use AVs and SAVs. Additionally, the estimated parameter for
SAVs outweighs those of AVs in all models except the Hungarian, which means those who
desire to share their daily transport mode with others have more acceptance of using SAVs than
AVs.
Respondents with more interest in new technologies like AVs, measured by the
“Technology_Interest” factor, have a positive attitude toward adopting AVs and SAVs. The
Hungarian model is the exception in this case, as it demonstrates that this variable was
associated with a significant negative tendency toward AV and SAV adoption; this can be
interpreted according to Schade and Schlag, who stated that having more information about the
scheme might lead to higher evaluation and assessment, and consequently higher reluctance in
accepting it [66]. In both Jordan and Ukraine, the “Cost_Oriented_Users” factor that represents
respondents sensitive to the trip cost (i.e., those who regard trip cost as the most important
factor in planning a trip) are more likely to choose AVs and SAVs. Moreover, the estimated
parameter associated with SAVs is greater than AVs, reflecting a higher tendency to use SAVs.
A probable reason for these findings could be that people assume AVs optimize the road
network, decrease travel time, and reduce travel costs [122], [131], [150]. In contrast, an
opposite attitude is found in the Hungarian model, where respondents were less likely to choose
AVs and SAVs. This significant negative relation in the Hungarian model could be due to
concerns about the high prices of new technologies, as is consistent with previous research [8],
[150], [151]. Those who consider traffic-related issues as major or serious problems (e.g.,
congestion, air pollution, and traffic noise) are less likely to choose AVs and SAVs. This
disutility could be explained by considering that respondents do not consider self-driving
vehicles to be helpful in reducing such traffic problems. An exception is found in the Jordanian
model, where this variable was associated with significant positive attitudes.
For the respondents in Jordan, Hungary, and Ukraine, the factor
“RP_Perceived_Anxiety” was associated with a positive attitude toward adopting AVs and
SAVs. Those who prefer changing their route instead of paying tolls are more likely to choose
AVs and SAVs; on the contrary, this factor was associated with a negative attitude toward using
AVs for Brazilian respondents. The “Equity” factor was statistically significant and associated
with a positive attitude toward choosing AVs and SAVs. Respondents who realize that RP will
provide them with benefits are more likely to use AVs and SAVs. It can be noticed that an
increase in the level of education also increases the likelihood of choosing AVs and SAVs. The
results do not demonstrate a strong relationship between age and the tendency to use any of the
hypothetical alternative modes. A negative statistically significant relationship associated with
an increase in age is only revealed in the Brazilian model, which means that older individuals
are relatively less likely to select AVs and SAVs. This stands in contrast to the assumption that
AVs could be an attractive transport mode for the elderly [116]. Gender was not statistically
significant in any of the models except for the Jordanian models, where males were significantly
associated with a negative attitude toward adopting AVs and SAVs.
53
3.6.3 Result’s Summary
This section summarizes the effects of the investigated factors on the acceptability of
RP and the adoption of AVs and SAVs. As shown in Table 14, there is heterogeneity in the
significance of these factors as well as their effects, whether positive (✔) or negative (✖) or
insignificant (empty cells). The factors behave differently in the four countries. Although most
of the factors are significant in most countries, none of the factors managed to significantly
affect RP acceptability, AV choice, and SAV choice in all countries at once. Considering the
significant factors only and looking closely at their impact on each of the three dependent
variables (RP acceptability and AV and SAV choice) separately, it can be noticed that few of
these factors have the same influence in all countries.
For example, in all countries, “Environmental_Oriented_Users” and
“Willingness_to_Share” factors have a positive impact on AV and SAV choices, respectively.
Similarly, the “Enjoy_Driving” and “AV_Safety_Security_Concerns” factors had a negative
impact on AV and SAV choice, respectively, while the influence of the other factors fluctuates
between positive, negative, and non-significant. This can be explained by the presence of
unobserved factors that affect people's behavior based on the circumstances of every country.
These results are logical as residents of every country have different perspectives, cultures,
economic situations, and other relevant characteristics.
Regarding the socio-demographic characteristics, the results show that they play a more
vital role in AV and SAV adoption than in RP acceptability. Moreover, in line with a previous
study [80], the results revealed that the attitudinal variables have more influence on RP
acceptability than socio-demographic characteristics.
Table 14. The relationship between the studied factors, RP acceptability, and future car choice in the four
investigated countries
Factor
RP
AV
SAV
BR
JO
HU
UA
BR
JO
HU
UA
BR
JO
HU
UA
Awareness
AV_Awareness
✖
✖
✔
✖
✔
✔
✖
✔
✔
✔
✖
RP_Awareness
✖
✔
✖
✔
✔
✔
Travel Behavior and Attitudes
PuT_Users
✖
✖
✖
✔
✖
Enjoy_Driving
✔
✔
✖
✖
✖
✖
✖
✔
Cycling_Users
✔
✔
✖
✔
✔
✖
Walkers
✖
✔
✔
✔
✖
✔
Technology_Interest
✖
✔
✔
✔
✔
✔
✔
✖
Environmental_Oriented_Users
✔
✔
✔
✔
✔
✔
✔
✖
✔
✔
✔
Cost_Oriented_Users
✖
✔
✔
✖
✔
✔
✖
✔
Sensing Traffic Problems
Sensing_Traffic_Problems
✔
✔
✔
✖
✔
✖
✖
✔
✖
Perceived Effectiveness
Perceived_Usefulness_RP
✔
✖
✖
✖
✖
✖
Negative_Expectations_RP
✔
✔
✔
✔
✖
✔
✖
✔
✖
✔
Personal Effectiveness
54
Willingness_to_Share
✔
✔
✔
✖
✔
✔
✔
✔
✔
✔
RP_Perceived_Anxiety
✖
✔
✖
✔
✔
✔
✔
✔
✔
Safety and Security
AV_Safety_Security_Concerns
✔
✔
✔
✖
✖
✖
✖
AV_perceived_ease_of_use
✔
✖
✖
Social norms concerning RP
acceptability
Social_Norm
✔
✔
✔
✔
✖
✔
✖
✔
✖
✔
Fairness
Fairness
✔
✖
✖
✖
✔
✔
Equity
Equity
✔
✖
✔
✔
✔
✔
✔
✔
✔
Background Characteristics
Age
✔
✔
✖
✔
✖
✖
Gender (ref: Female)
Male
✖
✔
✖
Education (ref: Less than
bachelor)
Postgraduate studies
✔
✔
✔
✔
✔
Employment (ref: Working)
Not Working
✔
✔
Use vehicle as driver (ref: Yes)
No
✔
✖
✔
3.7 Conclusion
In conclusion, this study investigates the effect of attitudinal factors and
sociodemographic characteristics on RP acceptability and AV and SAV adoption. The results
of the current study show that respondents from different countries have different behaviors
regarding RP acceptability and AV and SAV adoption. Such findings are in line with previous
research by Fürst & Dieplinger, who replicated AFFORD study [61] in Vienna to investigate
the factors that affect the acceptability of RP. They concluded that "both studies differ in terms
of influencing factors" [67]. In terms of RP acceptability, according to the responses obtained,
all investigated factors have a significant effect on RP acceptability in most or all of the studied
countries. An exception can be seen in the factor “AV_Perceived_Ease_of_Use” which is
significant only in the Hungarian model. Sociodemographic characteristics do not show a strong
significant relationship with RP acceptability. Among the factors used, it was found that the
following factors, “Environmental_Oriented_Users”, “Negative_Expectations_RP”, and
“Social_Norm” are statistically significant and positively affect RP acceptability in all countries
from the respondents’ perspective. Hungarian respondents would accept applying an RP
scheme regardless of the effect of other factors, while a negative tendency is found for
respondents from other countries.
Regarding AV and SAV adoption, the examined factors have a significant effect on the
respondent's adoption of AVs and SAVs in all or most of the countries. The
“Environmental_Oriented_Users” factor is positively significant in all countries except Ukraine.
55
Similarly, respondents willing to share their trips with others due to the application of RP are
more likely to use AVs and SAVs. It can also be seen that respondents with a high level of
education are more likely to adopt AVs and SAVs. The results do not demonstrate a strong
relationship between age and the tendency to use any of the presented alternative transportation
modes. Furthermore, the results show that respondents do not trust their government to use the
revenues from road tolls to support the state’s budget, and they prefer more clear and transparent
approaches to use RP revenues. Finally, the results show that the examined factors influence
the acceptability of RP and the adoption of AVs and SAVs, demonstrating the interrelationship
between them and the importance of their simultaneous study.
3.7.1 Insights for Policy Implication
The Results of this research can provide meaningful insights to stakeholders and
policymakers for anticipating and planning policy controls related to the adoption of AVs and
SAVs and RP acceptability in the transportation sector. It is difficult to generalize the policy
implications derived from this research due to the small sample size; however, some factors in
the analyzed models have almost the same effect on RP acceptability and AV and SAV adoption
across the investigated countries. We shed light on the effect of these factors in this section.
The results demonstrate that respondents’ awareness of new technologies and RP is an
important factor in their adoption and implementation. Therefore, educational campaigns
through different platforms and various methods should be held to inform people about the
expected benefits of driverless vehicles and RP, which will help raise their acceptability.
Similarly, the “Environmental_Oriented_Users” factor positively affects RP acceptability.
Gaining the support of this group by spreading the word about the environmental benefits of
implementing RP schemes will facilitate the authorities' task in applying such changes in their
countries. Additionally, respondents want their governments to use RP revenues in areas where
the residents can feel their impact, such as by enhancing PuT systems. Such policies are critical
as the respondent’s trust is very low in government entities regarding the use of revenues.
Therefore, it is advised to clearly explain the methods of utilizing the revenues from RP to
satisfy the public’s requirements so that authorities can reduce the trust gap with their citizens.
Furthermore, RP schemes that provide benefits to citizens are seen as more acceptable. Policy
makers should thus consider giving special attention to these aspects.
It is evident from the results that respondents who are willing to share their trips with
others are more open to accepting RP and using AVs and SAVs. This point can be utilized by
promoting and allowing ride-sharing services to operate freely; as the number of users of these
services increases, the acceptability of RP, AVs, and SAVs is likely to increase. Respondents
with safety and security concerns about AVs and SAVs are reluctant to use them. This
information can be used to devise policies to promote the safety and security features of AVs
and SAVs and reduce the anxiety of using these new travel modes. Most likely, the public will
initially resent the implementation of RP, AVs, and SAVs; the results of this research shed light
on the potential reasons for such rejection (e.g., safety and security concerns) and the positive
influencing factors (e.g., environmentally oriented users). Consequently, public agencies can
further elaborate on these insights to motivate the public to be in favor of RP, AVs, and SAVs.
Moreover, this research shows that neither RP acceptability nor AVs and SAVs adoption can
56
be generalized over a large population; therefore, city-specific policies will be necessary to
efficiently shift the transportation mode from CCs to AVs and SAVs and, similarly, to increase
the acceptability of RP.
3.7.2 Limitations and Directions for Future Research
This study paves the way to incorporate two research topics, RP acceptability and the
adoption of AVs and SAVs, into a single study. However, the research faces a set of limitations.
The first limitation can be identified in the sample size, which is relatively small compared to
the total population of the selected countries. This is likely a consequence of the utilization of
online questionnaires, which favor youth and individuals who have access to the internet.
Although the online questionnaire provided a video to establish a unified perception of meaning
for RP, AVs, and SAVs, it is still doubtful whether all the respondents have come to the same
conclusion after watching the short video. Therefore, the use of the results in the context of the
larger population groups should be carried out critically. Directions for further research are also
possible and highly recommended. There are several approaches in regard to future research.
Researchers can include new variables (e.g., AVs, SAVs perceived safety, AVs, SAVs legal
liability, or AVs, SAVs perceived comfort) and carry out research using a prototype of AVs
and SAVs alongside RP to gain more useful insights into how the presence of RP and the
inclusion of AV and SAV experience is likely to influence potential users to accept the concept
of RP, AVs, and SAVs. In addition, future research may use a stated preference experiment to
explore the effect of RP attributes (e.g., toll value) on AV and SAV adoption, which can give
more insight into how different RP tolls could affect vehicle adoption.
57
Related publications to this chapter:
M. Shatanawi, M. Hajouj, B. Edries, and F. Mészáros, “The Interrelationship between
Road Pricing Acceptability and Self-Driving Vehicle Adoption: Insights from Four Countries,”
Sustainability 2022, 14, 12798. https://doi.org/10.3390/su141912798.
M. Shatanawi, M. Ghadi, and F. Mészáros, “Road pricing adaptation to era of
autonomous and shared autonomous vehicles: Perspective of Brazil, Jordan, and Azerbaijan,”
Transportation Research Procedia, vol. 55, pp. 291–298, 2021,
https://doi.org/10.1016/j.trpro.2021.06.033.
Thesis II
I found that people who enjoy driving and have concerns regarding automated
vehicles' safety and security are less likely to choose self-driving vehicles. In contrast,
people who care about the environment while traveling are more willing to accept road
pricing and prefer self-driving vehicles, and those with more knowledge of and interest
in new technologies are more likely to opt for self-driving vehicles over conventional
ones. There is a positive impact of the willingness to share personal trips with other
passengers on road pricing acceptability and self-driving vehicle choice, with more
tendency to use shared autonomous vehicles. Family and friends can positively
influence related people to accept road pricing schemes. Furthermore, I demonstrated
the interdependency between road pricing acceptability and self-driving vehicle
adoption and the importance of their simultaneous study. Using surveys in four
countries, I determined the factors and socio-demographic characteristics affecting
road pricing acceptability and self-driving vehicles' adoption utilizing dimension
reduction techniques and regression analysis.
58
4 Chapter Four - Implications of the Emergence of Autonomous
Vehicles and Shared Autonomous Vehicles: A Budapest
Perspective
4.1 Introduction
As mentioned in the introduction section of Chapter three, the advent of AVs and SAVs
is projected to enhance network performance and accessibility. However, The future share
distribution of AV and SAV is not yet apparent, as is which of these two future modes will
become the dominant transport mode. This chapter compares the impact of deployment of AV
and SAV on Budapest’s network traffic performance and consumer surplus (CS) in alternative
future traffic scenarios to the Base scenario, which describes the traffic situation in Budapest
based on projected travel demand for the year 2020. Three future scenarios for the years 2030
and 2050 are presented and characterized by different penetration rates of AVs and SAVs to
reflect the uncertainty in market penetration rates of future cars. First, the “Mix-Traffic”
scenario for the year 2030 is shown, in which CC, AVs, and SAVs operate together on the
network. The other two scenarios comprise AVs and SAVs only and are assumed for the year
2050, where the "AV-Focused" scenario represents high dependency on private-owned AVs,
and the "SAV-Focused" scenario represents high usage of SAV fleets.
Several research papers discussed the implications of the emergence of AVs or SAVs
on Budapest network. For instance, Obaid and Torok investigated the impact of replacing CC
with private AV on Budapest’s traffic parameters and emission [161], [162], while Hamadneh
and Domokos studied the impact of the advent of SAV on traveler’s behavior [163]. Lu et al.,
on the other hand, investigated the potential change in the macroscopic fundamental diagram
due to the emergence of AV at different percentages using a network in Budapest and an
artificial grid road network [164]. However, to the best of my knowledge, this thesis is the first
to integrate both transport modes (i.e., AVs and SAVs) into the Budapest network and
investigates their implications on traffic parameters and consumer surplus simultaneously.
Visum is a traffic macroscopic simulation software, which was adopted in this study utilizing
the SBA for the city of Budapest to answer the following research questions:
1. What effects do AV and SAV deployments in Budapest have on the following traffic
performance parameters: average and maximum queue lengths, delays, volume, density,
utilization (scaled density), velocity, and vehicle kilometers traveled (VKT)? What are
the implications of implementing AVs and SAVs concerning consumer surplus?
2. How do varying the share distribution of AVs and SAVs affect traffic performance and
consumer surplus?
The rest of the work is presented as follows: An explanation of the Budapest network
model, as well as the SBA framework for AVs and SAVs, which was built using the Visum
software and utilized in this study, Section 2. Then, delving deeper into the development of
future traffic scenarios in Section 3. After presenting and discussing the results in Section 4,
the research’s conclusions were emphasized in the last section.
59
4.2 Research Framework
After providing an overview of the Visum software and the Budapest transport model
used in this chapter, this section describes the dynamic traffic assignment framework; the
detailed modeling of AVs and SAVs was used here.
4.2.1 EFM Macroscopic Model on PTV Visum
The city of Budapest is the capital of Hungary, with a population exceeding 1.76 million
within an area of 525 km2 and a destination for more than 4.61 million tourists every year [165].
Budapest’s transport network was modeled using PTV Visum software. Visum is a traffic
macroscopic simulation software that has the capabilities of modeling different transport modes
and simulating their traffic demand using estimated Origin-Destination (O-D) matrices and
considering a defined road network. It allows the user to import data of the network from
various sources, such as the Open Street Map. Moreover, it is used in designing and analyzing
transportation systems, projects, and solutions, including individual and collective traffic flows.
It is based on the popular approach of four-step modeling (viz. trip generation, trip distribution,
mode choice, and traffic assignment). The Visum software was chosen to design and evaluate
the different RP strategies in alternative future traffic scenarios. The reasons behind selecting
it are: (1) the nature of the analysis, which is a macro-simulation that relies on the various
characteristics of traffic flow such as volume, delay, speed, and density, (2) the availability of
a ready transport network model for Budapest entitled "EFM_SV05_2020" (hereafter in this
thesis referred to as EFM) including all relevant information such as O-D matrices, road
capacity, traffic volumes, and free-flow speed among others in Visum, and (3) Visum has the
ability to deploy and analyze new transport modes (i.e., AVs and SAVs).
The EFM Model for Budapest and the agglomeration was developed by The Centre for
Budapest Transport (BKK) using Visum software [166]–[168]. It includes more than 30,000
links that represent both the roads and rails along with their actual traffic parameters, such as
the number of lanes, permitted vehicle type, and speed. Over 15,000 nodes show the start and
the end of each link and the intersections in the network. The developers of the model utilized
the land use to divide it into 1,200 zones and consider these zones the origin and destination for
the trips in the network. The model was developed and calibrated using the traffic status of the
year 2014, considering different parameters such as demographics, motorization, and economic
trends [55], [168], [169]. The EFM Model contains the three components of macroscopic
modeling (i.e., demand model, network model, and impact model). Moreover, the model
provides a forecasted demand for the years 2020, 2030, and 2050 considering demographic
projections, motorization projection, and economic growth impact; these projected demands
were utilized in the simulation process of the future traffic scenarios (see Section 3 for further
details). The developer of the model used the following traffic forecast equation.
60
where Ct is the level of motorization in the specific year; S is motorization saturation level
(saturation); C0 motorization level of the base year; g0 is growth factor; and t is the number of
years elapsed in relation to the base year.
Furthermore, the EFM Model is calibrated for all modes of private transport, taxis,
public transport, freight transport, and bicycles in a precise way to mimic a realistic
representation of the actual traffic behavior of the network elements with a small margin of
error, therefore providing reliable predictions. Approximately 240 cross-sections, distributed
throughout the city, were used to calibrate the O-D matrices, and the Geoffrey E. Havers (GEH)
function was used to define the expected tolerance. The results of the calibration showed that
the tolerance of the cross-sectional traffic volumes compared to the counted data is acceptable.
As this research applied the dynamic traffic assignment method instead of static equilibrium
assignment, an additional calibration was performed to ensure the model's suitability for the
use of dynamic traffic assignment. The GEH function was also adopted in the modified model
because it has a feature that the relative deviations decrease when the observed values increase.
It also places more emphasis on larger traffic volumes than on smaller traffic volumes [52],
[53]. Additionally, the GEH function is adopted by different road administrations, such as
FHWA in the USA, BKK in Hungary, and ARRB in Australia [54], [55].
To do so, I compared the values of the same 244 cross sections’ count data (used in the
original tuning) to the simulated traffic data in the modified model (using the SBA assignment).
The expected tolerance value was defined by the GEH function displayed in Equation 6.
(6)
Where is the newly simulated traffic data using the SBA assignment in the new
model, and is the same 244 cross sections’ count data (used in the original tuning). A
maximum of GEH 5.0 was defined in the modified model, similar to the value used in the
original one. The GEH values of the modified model are represented in Table 15.
Table 15. Summary of GEH Values
GEH values
Sections
GEH < 2.5
115
2.5 ≤ GEH < 5.0
95
5.0 ≤ GEH < 7.5
14
7.5 ≤ GEH < 10
17
GEH ≥ 10
3
Department for Transport [170] recommended a GEH index of less than 5 for at least
85% of the measured sections. Table 15 shows that 86% of the measured sections in this study
have a GEH index of less than 5, which shows a good match between the modeled and observed
traffic volumes. Figure 4 shows the sections used for the calibration with their corresponding
GEH values.
61
Figure 4. Graphical representation of the sections used for the calibration and their corresponding GEH values
Consequently, EFM Model is professionally designed and reflects a realistic model of
the Budapest transportation network; it can thus be used to plan, develop, and predict the
forthcoming transport changes in the city of Budapest and its surroundings.
For further information about the Visum Software and EFM model, see [55], [171].
4.2.2 Simulation of AVs Using SBA
The level of automation and the availability of suitable infrastructure are essential
factors in integrating the driving behavior of AVs and SAVs into the road network. AVs and
SAVs are able to communicate with each other if a network segment support Vehicle-to-
Vehicle (V2V)
2
and Vehicle-to-Infrastructure (V2I) communications, which allow the
automated vehicles to drive in a fixed headway or even to form platoons. Such network
segments are identified, in this research, as "S/AV-ready".
The SBA models use a traffic simulator to simulate complex traffic flow dynamics,
which helps design meaningful operating solutions for real-time implementation. In the SBA
assignment, the traffic flow distribution and spatiotemporal interactions are addressed through
simulation rather than analytical evaluation. SBA is a dynamic assignment procedure that
accounts for node impedances and allows users to model the forming and dissolving of queues
over time. This means that individual vehicles are simulated, and a simple car following model
is applied to have the vehicles follow their assigned paths. The assignment is an iterative
procedure involving route search, network balancing, and simulation.
The following two sub-sections discuss the modeling framework of AVs and the traffic
assignment parameters. Then the third sub-section provides an overview of the used utility
2
V2V communication was assumed in this research as the following vehicle adjusts its SBA reaction time factor
based on the transportation system of the vehicle in the front.
62
function, the adopted value of travel time (VOTT), and the calculation of consumer surplus and
social welfare.
4.2.2.1 Reaction Time under SBA
This study analyses the impact of different pricing strategies (static and dynamic) on
the traffic performance parameters (TPP) and total change in social welfare in the era of AVs
and SAVs. The first step was to deploy AV and SAV behavior into the Budapest network using
SBA. PTV Visum uses the car following model to simulate the network, which identifies the
following vehicle behavior. This behavior can be adjusted in Visum by using the link attribute
“SBA reaction time factor”, which calibrates the link’s capacity. However, this factor is applied
universally to the reaction times of all vehicles on the link. In AV and SAV contexts, the
following behavior depends on the vehicle itself as well as the one in the front. For instance,
the headway is shorter when two self-driving vehicles follow each other than AVs or SAVs
driving behind CCs. Therefore, a more detailed link attribute that considers the transport system
of the vehicle itself and the transport system of the vehicle in front is required, such as "SBA is
reaction time factor transport system dependent", to identify the following vehicle's behavior.
The attribute "SBA is reaction time factor transport system dependent" allows assigning
different reaction times for different types of vehicles separately. Generally, the SBA factor for
CCs in normal conditions is equal to 1. By increasing the automation level in the vehicles, this
value decreases because of the lower reaction time needed for braking. This entails shorter
headway and higher link capacity, such as in the case that there are two links with similar
characteristics like the number of lanes and speed limit, but the SBA factor for the first link is
1.2 and 0.6 for the second link. Then the capacity of the second link is larger than the capacity
of the first one because of the tighter headways, which is a consequence of a shorter reaction
time.
This thesis considers Level 5 automated vehicles while focusing solely on private
transport and adjusts the following vehicle's behavior by changing the SBA reaction time factor
based on the transport system of the leading vehicle. Therefore, considering the possible
combination of the transport system, six categories specifying the SBA reaction time factor
were assumed. Table 16 shows that the value of the SBA reaction time factor is the lowest when
both leading and following vehicles are automated (i.e., 50% shorter reaction time compared to
CC). The SBA reaction time factor value increases if the leading vehicle is not autonomous, as
illustrated in categories 4 and 5, where the reaction time is assumed to decrease by 35%, and it
is assumed to be 1 between other transport systems.
Table 16. SBA reaction time factor values
Category No.
TSys Combination
SBA reaction time factor –
PrTSys-PrTSys
Leading Vehicle
Following Vehicle
1
AV
AV
0.5
2
AV
SAV
0.5
3
SAV
AV
0.5
4
Other TSys
AV
0.65
5
Other TSys
SAV
0.65
6
Other TSys
Other TSys
1
TSys: Transport System, PrTSys: Private Transport System
63
The assumed values of the SBA reaction time factor for the possible transport systems
combinations were calculated based on the expected increase in the capacity resulting from the
emergence of AVs and SAVs. The assumed penetration rates for AVs and SAVs, in this
research, are 50% and 100% for the years 2030 and 2050, respectively. Previous studies adopted
a module to reflect the increase in capacity resulting from tighter headways and lower reaction
time needed by automated vehicles. They found that the capacity increases by a factor of 1.2
and 1.5 in the case that the penetration rate of the automated vehicles are 50% and 100%,
respectively [9], [172]. The capacity in SBA can be calculated from the following equation
[171].
where is the SBA capacity [veh/hour], is the number of lanes, is the SBA effective vehicle
length [m], is the link’s velocity [km/h], and is the SBA reaction time factor. The required
change in the capacity (i.e., 1.2 and 1.5) was achieved by setting values to 0.65 and 0.5,
respectively. Although there are other input parameters for the car following model, such as
“free-flow speed” and “SBA effective vehicle length” [171], it was adequate in this research to
adjust only the SBA reaction time factor to model AVs and SAVs. However, such aspects can
be further analyzed in future work on this subject.
4.2.2.2 SBA Parameters
The Complex traffic flow can be simulated using SBA models, which provide helpful
operational solutions for real-time applications [173]–[177]. Three steps are required to develop
the SBA model. The first is a network that was created and designed professionally in the EFM
model (see Section 4.2.1). In this step, the network was modified to be S/AV-ready, which
distinguished automated vehicles from conventional ones by identifying the characteristics of
each transport system (i.e., CC, AV, SAV) separately across the network. The short links were
avoided by setting the minimum link's length to 7 meters.
The second step is to define the analysis period (AP). The EFM model defined the
standard time series by percentage share, where a weight is considered for each time interval.
This research set the morning peak hour (7:00 – 8:00) as AP, where this period has the highest
share of the demand for one hour for private transport modes as a ratio compared to daily traffic
demand and equals 7.15%. This period represents the worst-case scenario; therefore, the system
will withstand other periods as it is designed for the most severe one. Moreover, for a more
detailed time evaluation of calculation results, I defined an analysis time interval (ATI) to 10
minutes during the AP. The analysis period of Traffic assignment is one hour, and the
assignment was fulfilled every 10 minutes.
The result's reliability of the SBA model is determined by comparing the deviation in
volume pattern for each iteration to a predefined tolerance rate, which specifies the allowable
error in the final solution. Lowering the tolerance level provides more precise results but
increases the computational time significantly [178]. Consequently, the third step is the SBA
procedure, which includes setting several parameters that control traffic assignment results.
One of the most relevant SBA parameters used in this research is the Termination Condition
64
which defines the duration of the traffic assignment. The assignment stops once one of two
conditions is achieved, viz. maximum number of iterations or maximum gap. A higher iteration
number produces more realistic and reliable results but requires more computational time. In
this research, considering the computational limitations, the maximum number of iterations and
the maximum gap are 10 and 0.01, respectively. In certain cases, the maximum gap is reached,
but there are still some vehicles in the network, so the termination condition is not fulfilled. A
third condition is predefined and calculated here, viz. maximum number of additional iterations,
which ensure that all vehicles have exited the network after the last iteration.
The SBA allows the use of daily distribution of the traffic to obtain better results, and I
applied all necessary modifications to ensure the suitability of the EFM model for using the
dynamic traffic assignment and getting realistic results.
4.2.2.3 Utility Function, VOTT, and Social Welfare
Utility function normally depends on cost, time, or distance. In the EFM Model, these
three criteria are applied in a mixed way. Distribution parameters are used to distinguish using
time or distance in different cases and determine the ratio of time and distance for individual
motorists and public transport branches in each case. Moreover, the EFM model updates these
parameters repeatedly (including car travel distance and access time, public transport distance
and travel time, number of transfers, and parking fees) between origin and destination zones to
obtain more accurate results. The utility function is applied in the third step of building the
model "Mode Choice" to differentiate traffic by transport modes between origin and destination
points. This procedure is performed using a logit model in which users opt for achieving the
highest possible utility of making a trip. Equation 8 shows the utility function for car users in
the model "UCar". The constant and factors in Equation 8 were calculated based on a Stated
Preference Survey distributed during the EFM model development process.
where ASC is Alternative-specific Constants, β is the factor that shows the sensitivity of each
variable, PC is parking cost, PT is parking search time, and WT is walking time. The value of
each sensitivity factor is presented in Table 17.
Table 17. Utility function factors values for private transport
Constant/
Factor
ASC
βcost
βtime
βPC
βPT
βWT
Value
1.05
-0.002
-0.1
-0.015
-0.1416
-0.0904
In the assignment procedure, the impedance function is used to assign the route choice
for every user. Generally, the impedance function calculates the travel cost parameters such as
trip distance and time to determine the route for users. In our research, the selected parameters
to calculate the impedance are trip length, travel time (tcur), toll amount, and Tehertilt which
corresponds to the “Freight Transport Strategy for Budapest” by setting up weight-restricted
zones for freight vehicles to control the vehicles entering the city to mitigate environmental
impacts. In Visum, the SBA works with a reaction time factor instead of a volume delay
function (VDF), which is used in a static assignment. Therefore, static assignment controls AV
65
implementation by altering the Passenger Car Unit (PCU), which results in changing the travel
time on links thanks to VDF. In contrast, using the reaction time factor in SBA for implementing
AVs would primarily do AV implementation in terms of capacity. This research adopted SBA
in the simulation process as it has multiple advantages compared to traditional traffic
assignments. For instance, modeling traffic flow using SBA correlates travel time and
congestion; if the link inflow is greater than the link outflow, a higher density and a lower speed
on the link will occur; in contrast, static assignment methods do not directly link traffic
throughput with any physical measures characterizing congestion like speed or queue [179]. In
addition, SBA exemplifies travel choices with more details and is capable of investigating
various advanced traffic management systems [180].
VOTT is a crucial factor in the modeling process, as it has the ability to convert the time
dimension into monetary value. This value would be integrated into the generalized cost
function, which eventually determines the route choice for users. In AV and SAV contexts,
VOTT decreases as users can utilize their travel time during the trip by performing other
activities such as working, reading, and sleeping rather than driving and monitoring the road.
There are various studies that have investigated the possible reduction of VOTT in the era of
AVs and SAVs using different methods, mainly by assuming one or more coefficient values
for the possible reduction in VOTT [2, p. 53,54]. In this research, AVs are considered to be
privately owned vehicles, while SAVs are used in the form of a dynamic demand-responsive
ride-sharing system, which means that the AVs would have a higher level of comfortability,
privacy, and freedom (i.e., less restrictions) than SAVs. Therefore, the VOTT for AVs and
SAVs is assumed to be 50% and 75% of the VOTT of CCs, respectively. The VOTT for CC
was extracted from the recent CBA guide on transport infrastructure projects in Hungary, and
it is equal to 6871 HUF
3
/hour for a work-related trip. Finally, VOTT varies across users (e.g.,
high-income households vs. low-income households) and differs based on trip purpose (e.g.,
work-related travel vs. non-work-related travel); considering these variables will result in more
accurate results. However, it was not possible in this research to divide users into different
classes nor to distinguish trips according to specific characteristics, as it falls beyond the
research scope, and this gap can be an extension of this work in the future.
It is essential to evaluate any policy in terms of measured benefits for both users and
operators to estimate the efficiency of the proposed strategy considering the welfare change.
The user's benefits can be calculated using the economic concept viz. consumer surplus.
Consumer surplus measures the change in user's benefits generated from implementing
proposed intervention policies like RP by calculating the variation of user's costs such as trip
time and cost between the "Do-Something" scenario (in this research, after AVs and SAVs
emergence or applying RP strategies) and "Do-Minimum" scenario (without any intervention).
To that end, the use of the Rule of Half (RoH) is suitable here, as it provides an approximation
of how the applied RP strategies would change consumer surplus [181]. This is according to
Winkler [182], who proved that the use of the RoH considering the observable costs only (i.e.,
travel time and travel cost) is right. Consequently, the consumer surplus in this research is
calculated as shown in Equation 9.
3
HUF is Hungarian Forint and 1 HUF = 0.0031 United States Dollar [Google, January 24, 2021]
66
where ∆CS is the change in consumer surplus, and are the generalized cost of the trip
before and after the intervention, respectively, are the number of trips before and
after the intervention, respectively. The generalized cost function of the trip () is calculated as
shown in Equation 10.
where in this situation, the cost and travel time attributes only vary as a result of the intervention
and can be obtained from a distance traveled during the trip in kilometers, the cost of operation
per kilometer, which equals 24.45 HUF/km according to BKK, the travel time in hours, and the
VOTT.
4.2.3 Simulation of SAVs Using SBA
A dynamic demand-responsive ride-sharing system (DRS) was applied to design the
SAV system. This necessitated disaggregating the zone demand (i.e., O-D matrices) down to
node level by dispatching the zone demand through a stochastic process to the assigned nodes
within a buffering range of twenty meters that defines each trip request's spatiotemporal
characteristics. This process was performed using the Generate Trip request procedure in Visum,
where each trip request involves starting and destination nodes, pick-up, and drop-off nodes,
and desired pick-up and arrival time windows. Moreover, this research considers that the SAV
fleet comprises pure-electric vehicles before each assignment due to their expected benefits and
high penetration rates [29], [30]. The following two sub-sections illustrate the simulation
framework of the SAV system and the SAV supply modeling.
4.2.3.1 Simulation Framework of the SAV System
The DRS for SAVs operates as follows:
• The passenger requests a trip at a given time and location.
• According to the spatial proximity scheme, SAV is assigned (at a configured parking
facility) to the trip requests. It is noteworthy that the assigned SAV goes to the nearest
pick-up/drop-off point (Pu/Do) and not the trip's request exact location.
• The shortest path search is performed for both the SAV and passenger from their
location to the pick-up point.
The assigned vehicle needs to reach the pick-up point within the maximum arrival time
according to the following equation.
where AT is arrival time, EDT is the earliest departure time, IDT is the ideal travel time, DF is
the detour factor, ADT is the always accepted detour time, and MDT is the maximum detour
time.
67
The detour factor is the actual (with detouring) travel time divided by the ideal (without
detouring) travel time, where we assumed that the maximum detour factor is 2, the maximum
detour time is 30 minutes, and the always accepted detour time is 10 minutes
• If the SAV fulfills the maximum arrival time condition and reaches the pick-up point,
the passenger boards the SAV.
• The SAV departs to the nearest drop-off point to the passenger destination using the
shortest path.
• The system searches for in-route requests during the trip (in the previous step) based on
vehicle capacity, power constraints, and accepted detour factor.
• As soon as the passenger leaves the SAV at the nearest drop-off point, the passenger
takes the shortest path to the final destination on foot, and the SAV continues to the next
Pu/Do using the shortest path as well.
• The level of electric power in SAVs is checked before each assignment to ensure its
ability to complete the assigned trips successfully, where the maximum range per charge
is 150 KM for a 4-seat SAV. Otherwise, SAV will be assigned to the nearest charging
station.
• If any vehicle stays inactive for more than 30 minutes, it will be assigned to the nearest
charging station or parking facility.
4.2.3.2 SAV Supply Modeling
The supply modeling of the DRS of SAVs comprises infrastructure and the SAV fleet.
The supply modeling of the infrastructure involves identifying the SAV service area in the
network, the operating network, parking facilities, charging stations, and the Pu/Do.
Simultaneously, the supply modeling of the SAV fleet consists of specifying the SAV fleet size,
the SAV fleet characteristics, on-road behaviors, and interactions with other vehicles. The
remainder of this section illustrates the most important components of the supply modeling in
more detail.
The service area of SAVs covers 475 km2 of Budapest and consists of 906 zones. Most
of these zones have moderate to high demand density and include the inner-city demand. SAVs
are permitted to leave the designated area during operation; however, the loading and unloading
of passengers are allowed inside the designated area only.
SAVs and passengers are allowed to move on private vehicle links and permitted
walking paths, respectively, while using other link types is not permitted. The walking from
passenger location to pick-up point and from drop-off point towards the passenger's final
destination was distinguished by using the function walking transport mode [DRT Walk] in
Visum. Figure 5 represents the SAV service area as well as the SAV walk network.
68
Figure 5. The service area and the walk network for SAV
Accessible Pu/Do by foot and private vehicles were created within the SAV service area
at 1979 nodes. Although door-to-door service is preferred by users and expected in the era of
SAVs, the simulation environment in our model does not allow it, which necessitated creating
Pu/Do through the network.
The size of the fleet (i.e., the number of operating and idle vehicles) must be determined
to meet the demand of users (generated trip requests) effectively. Therefore, the optimal SAV
fleet size needed to serve the demand inside the service area within the analysis period was
determined, in every scenario, by executing a warm-up simulation comprised of twenty various
random seed
4
values of the function "Generate Trip Request and Tour Planning Procedures".
This process produced twenty different values for the required fleet size; moreover, these values
reflect the numbers and Spatio-temporal randomization of trip requests. The average of the
obtained fleet size values was adopted as the size of the SAV fleet in each scenario, where the
fleet size of SAV was 1100 in the Mix-Traffic scenario, 1640 in the AV-Focused scenario, and
4387 in the SAV-Focused scenario.
Holding area in the developed model of this research refers to both parking areas and
charging facilities. The parking areas were allocated at seven nodes (currently existing parking
places in Budapest) within the service area, noting that different studies anticipated a reduction
in the required parking facilities, especially in the city center, with the advent of SAVs [183].
As mentioned earlier in this thesis, the SAV fleet consists of all-electric vehicles. Consequently,
charging stations were inserted into the service area at ten nodes, mainly situated around
parking areas, gas stations, and already existing charging spots for electric vehicles.
Furthermore, the charging duration and battery level are checked before each assignment,
where the charging duration of the battery from 0% up to 80% takes 4 hours and from 80% up
to 100% requires 3 hours. To achieve more efficient service of the SAV fleet, an optimization
4
“The value initializes the random number generator. Two procedure sequences with the same version file and
identical seed random numbers are executed in the same way. If the random seed is varied, the stochastic
functions are assigned to a different value sequence in Visum, so that the result of distributing the trip requests to
the nodes changes. This can lead to different distribution results.” [171].
69
for the selection of the locations of SAV facilities, including charging, parking, and Pu/Do
points, is required, and it can be executed through the Capacitated Facility Location Problem
(CFLP). However, applying such an application would extend the scope of the research, and
therefore this limitation was left for future development on this study. Figure 6 shows the Pu/Do
points and the holding area, including parking and charging facilities.
Figure 6. The location of holding areas and Pu/Do points within the SAV service area
The adopted SAV model in this research depends on a real-time sharing system. In other
words, the dispatcher receives the information about the Spatio-temporal of trip requests only
when a passenger requests a trip, noting that booking an SAV in advance is not possible here.
Therefore, a module that allows dynamic online dispatching is required. The vehicle routing
problem (VRP) is suitable in this case as it provides the possibility of dynamically serving the
maximal number of trip requests by utilizing the SAV fleet [184]. The VRP module was
deployed within the tour planning procedure in Visum, which connects the generated trip
requests (demand), SAV fleet (supply), and SAV infrastructure (holding areas and Pu/Do points)
for the dispatching process. Various constraints were considered while executing the tour
planning procedure; some of them were addressed before in this section, including vehicle
capacity, charging duration of the battery, maximum detour factor and time, maximum range
per charge, and inactive time. Other constraints were related to passengers, including the
maximum allowed waiting time as well as desired pick-up and arrival time. All these constraints
were considered within the assignment process.
4.3 Future Traffic Simulation Scenarios
This study compared the impact of the emergence of AVs and SAVs on traffic and
consumer surplus in three distinct future traffic scenarios to the Base scenario. The three future
scenarios attempt to cover the different possibilities of the emergence of AVs and SAVs in the
years 2030 and 2050 in the city of Budapest. The travel demand of the developed scenarios was
obtained from BKK projections for the respective years. The total forecasted demand remained
70
the same, but the private transport mode (i.e., passenger cars) was replaced, partially or entirely,
by the assumed share of AV and SAV for each scenario. To produce O-D matrices for AVs and
SAVs, all cells in O-D matrices of private transport demand were multiplied by the respective
penetration rates for AV and SAV in every scenario and with respect to their service area zones.
At the same time, the CC percentage in private transport demand was decreased by the same
percentage of AV and SAV penetration rates. The total forecasted private transport demand in
the years 2030 and 2050 was 2.23 and 2.31 million trips per day, respectively. This research
considered replacing the private transport modes solely with AV and SAV while assuming no
changes would occur in the modal share and analyzing the impact of the pricing strategies on
network performance and social welfare after applying these changes.
The first scenario, "Mix-Traffic Scenario", combines CC, AV, and SAV modes
altogether, considering the projected demand of 2030 for Budapest. It is expected that by the
year 2030, full self-driving automated vehicles will be on the streets [2], and travelers may
change to using AV and SAV; however, the penetration rates of automated vehicles could be
limited due to high prices [3], [4], [185]. Therefore, the dominant mode of private transport in
this scenario is CC, having a 50% share of the private transport demand, followed by AV and
SAV with 40% and 10% of the demand share, respectively. Figure 7 shows the simulation area,
which represents the Budapest network and its surroundings, where the service area of SAVs
(highlighted in pink and covering Budapest) is smaller than the area of AVs, which covers
Budapest and its surroundings because the zones with moderate to high demand density only
were considered in the case of SAVs.
Figure 7. Simulation area, including the Budapest network and its surroundings
The second and the third scenarios concern the full substitution of CCs by AVs and
SAVs. The forecasted demand of Budapest for the year 2050 by BKK was used in the
simulation process for these two scenarios. There are several studies that anticipate automated
vehicles to replace CCs, entirely or partially, by 2050 [3], [1]. However, the share distribution
of AVs and SAVs is still vague, and it is also unclear which of these two distinct future modes
71
will be the dominant one [9]. Hence, two different scenarios representing the two possibilities
regarding the emergence of AVs and SAVs were developed. In the second scenario, "AV-
Focused Scenario", the projected demand for private transport in Budapest for the year 2050
was substituted with AVs and SAVs at 85% for AVs and 15% for SAVs. It is assumed in this
scenario that most car owners and those who have access to a private car will switch from CCs
to privately owned AVs, and only 15% of them will change to use SAVs instead of CCs.
Conversely, in the third scenario, "SAV-Focused Scenario", the SAV mode is assumed to be
largely available and characterized with high uptake at a share of 40%, and 60% for AV mode.
It is expected in the future that fleets of SAVs will operate on streets to meet travel
demand, as the companies that provide ride-hailing services are making major investments in
deploying SAV fleets in cities as an alternative transport mode for private and other modes
[186]–[188]. Finally, the assumed penetration rates of SAVs for the second and third scenarios
were selected based on a previous study by Chen & Kockelman [29], who anticipated that the
mode share of a fleet of electric SAVs in a midsize city under certain assumptions would range
from 14% to 39%, and the remainder of the mode share is allocated to AV mode.
4.4 Results and Discussion
The findings reported in this section show the influence of the introduction of AVs and
SAVs on TPP and the change in consumer surplus in the proposed future traffic scenarios
compared to the Base scenario. The examined TPPs are average and max queue length, delay,
volume, density, utilization, velocity, and VKT. The next paragraphs explain each of these
parameters and the change in consumer surplus.
The SBA queue length outputs show the average and maximum queue lengths (lane
average and lane max) on the link edges assigned to lanes in meters at each analysis time
interval (ATI). It is derived by multiplying the average accumulated vehicle length on the lane
during the ATI by the proportion of time spent waiting for vehicles at the end of a lane divided
by the ATI. The effect of implementing AVs and SAVs in the network on average and
maximum queue lengths differs according to their penetration rates. Considering the summation
of average queues accumulated on each link in the network for every scenario at 8:00 AM
shows that the highest value occurred in the Base scenario, where the summation of average
queues was 60 km. This value decreased significantly when deploying AVs and SAVs in the
road network, by 78%, 93%, and 99% for Mix-Traffic, AV-Focused, and SAV-Focused
scenarios, respectively. SBA handles queues dynamically and passes on congested vehicles to
the next time interval. In the case of the last ATI, the queue would be dissolved in the extension
time interval. Noting that, the smaller SBA reaction time parameter changes the behavior of
following vehicles by reducing the headways; consequently, more vehicles can pass over a link
in one hour before queues form.
The SBA Max queue length shows the maximum queue length accumulated on each
link at each ATI. Figure 8 depicts the summation of the average queue length on the left y-axis
and the maximum of the SBA Max queue length on the right y-axis for each scenario in the
whole network at 8:00 AM. The maximum of max queue length (i.e., the longest queue occurred
in the network at 8:00 AM in every scenario) followed a similar pattern to average queue length,
72
where it decreased by 44%, 45%, and 95% in Mix-Traffic, AV-Focused, and SAV-Focused
scenarios, respectively. In a wider perspective, the percentage change in the summation of SBA
max queue lengths on all links in the network yields again a similar pattern of the change in the
summation of average queue length with approximately the same percentages.
Figure 8. SBA queue length (summation of average and maximum of max) @ 8:00 AM for all scenarios
The SBA calculates delay by comparing the travel time in a network with no volume
(t0) to the average travel time when the volume is taken into account (tcur) during the AP. Figure
9 shows the percentage changes in delay due to emerging of AVs and SAVs into the road
network for each scenario. The percentage change in delay illustrates that the deployment of
self-driving vehicles into the road network reduced the delays significantly. In the Mix-Traffic
scenario, the delays were decreased by 77%, and a further reduction took place in AV-Focused
and SAV-Focused scenarios at 94% and 97%, respectively. The reason behind such reduction
refers to the reduction in queue lengths, which implies that vehicle waiting times at the end of
the links is much smaller, and the reduction in traffic volumes as a smaller number of SAVs
replaced many CCs, which resulted in fewer traffic volumes on the links and smaller difference
between t0 and tcur, consequently reducing the delays. It is noticed that the emergence of AVs
and SAVs in the network reduced the summation of average queue lengths and the delays by
almost the same percentage in every scenario.
60.00
13.00
4.01
0.44
508.7
280.1 284.3
23.6 0.0
100.0
200.0
300.0
400.0
500.0
600.0
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
Base Mix-Traffic AV-Focused SAV-Focused
Maximum of Max Queue length [m]
Summation of Average Queue length
[km]
Summation of Avg
SBA Max queue
73
Figure 9. Delay in each scenario during the AP and Percentage change of traffic delay for three proposed future
scenarios compared to Base scenario
The SBA calculates the volume for each link in the network as the count of vehicles that
crossed the network links during the AP [Veh/hour]. Although implementing AVs would cause
shorter headways, which will most likely generate more capacity due to better utilization of the
roads, resulting in more vehicles passing through a certain point within a certain time unit (i.e.,
capacity), the traffic volume has decreased in the investigated future traffic scenarios. The
reason behind this reduction is associated with replacing CCs with SAVs. As 1100, 1640, and
4387 SAVs served 15945, 24775, and 66066 trips of private travel demand during the AP in
the Mix-Traffic, AV-Focused, and SAV-Focused scenarios, respectively. Figure 10 shows the
total volume in the network in all scenarios during the AP and the percentage reduction in the
volume for the proposed future traffic scenarios compared to the Base scenario. The volume
decreased with increasing the penetration rate of AVs and SAVs, and the maximum reduction
was reached in the SAV-Focused scenario at 45%.
Figure 10. Total volume in each scenario during the AP and Percentage change of traffic volume for three
proposed future scenarios compared to Base scenario
SBA density is a simulation’s output that refers to the average number of vehicles per
kilometer on the link during ATI. Similar to the previously investigated TPPs (i.e., queue length,
59.5
13.9
3.7 1.8
-77%
-94%
-97%
-120%
-100%
-80%
-60%
-40%
-20%
0%
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
Base Mix-Traffic AV-Focused SAV-Focused
% Changes in delay compared to Base
Delay [Hours]
Delay (Hours) % change in delay
9.723 9.062 8.594
5.336
-7%
-12%
-45% -50%
-40%
-30%
-20%
-10%
0%
0.0
2.0
4.0
6.0
8.0
10.0
12.0
Base Mix-Traffic AV-Focused SAV-Focused
% Change in Volume compared
to Base
Total Volume [Million
Veh/hour]
Total volume % Change in volume
74
delay, and volume), the density was reduced when including AVs and SAVs in the simulation
procedure. The reduction in average traffic density during the AP compared to the Base scenario
was 16%, 25%, and 55% for the Mix-Traffic, AV-Focused, and SAV-Focused scenarios,
respectively. Figure 11 illustrates the reduction in average density resulting from the emergence
of AVs and SAVs during AP at each ATI. It is evident that a higher replacement rate of CCs
by SAVs affected the average density more; for instance, the average traffic density at 8:00 AM
in the Base scenario was 5.8 [Veh/km], and it was reduced to 4.2, 3.4, and 2 [Veh/km] for the
Mix-Traffic, AV-Focused and SAV-Focused scenarios, respectively
Figure 11. Average traffic density for all scenarios
SBA utilization is an output attribute obtained during the simulation and corresponding
to scaled density. It shows how much of the link’s capacity is being used based on the
fundamental diagram, which connects volume and density values. A visualization of the SBA
utilization attribute at 8:00 AM that compared the three proposed future traffic scenarios to the
Base scenario is depicted in Figure 12. The green color in the figure illustrates less traffic on
the links allowing more cars to use it until reaching the critical density; hence utilization is
improved, whereas the red color shows the opposite. The most significant effects on utilization
occurred in the city center and on the ring around Budapest in all scenarios, with a noticeable
increase in the green color and diminishing red color as the replacement rate of CCs by AVs
and SAVs increases. The comparison showed that the maximum improvement in the network
took place in the SAV-Focused scenario, followed by AV-Focused and Mix-Traffic scenarios.
Mix-Traffic
AV-Focused
SAV-Focused
Figure 12. The change in SBA utilization after deploying AVs and SAVs compared to the Base scenario
SBA velocity attribute is calculated from SBA length, including the vehicle and the link
lengths, and the average travel time of vehicles that crossed the link during the AP. Then, the
0
1
2
3
4
5
6
7
7:00 7:10 7:20 7:30 7:40 7:50 8:00
Average Density [Veh/km]
AP [Hour:Minutes]
Base Mix-Traffic AV-Focused SAV-Focused
75
average velocity of all vehicles used that link is sorted in Visum as a link attribute. The average
vehicles’ velocity in the whole network (i.e., the average of all link’s average velocities)
increased with the advent of the AVs and SAVs.
The average vehicles’ velocity for all links in the network and the percentage increment
in the velocity in the future traffic scenarios compared to the Base scenario are depicted in
Figure 13. The velocity increased with the emergence of AVs and SAVs by 2% in the Mix-
traffic scenario, 4% in the AV-Focused scenario, and more increment took place in the SAV-
Focused scenario to reach 5% compared to the Base scenario.
Figure 13. Average velocity in each scenario during the AP and Percentage change of traffic velocity for three
proposed future scenarios compared to Base scenario
In reference to the Base scenario, the results revealed that VKT increased in the Mix-
Traffic scenario by 18%. On the contrary, it decreased in the AV-Focused scenario by 2% and
by 36% in the SAV-Focused scenario. The reduction in the VKT was associated with the
reduction in the volume. It is worth mentioning that if the boarding passengers in SAV have
the same destination, the trip is considered one trip. Otherwise, passengers with different
destinations are considered as several trips. For example, if two passengers board together and
head towards the same destination, this is counted as one trip of the demand; but if these two
passengers have two different destinations, they are counted as two trips of the demand. This
shows the effect of SAVs on reducing the number of private transport trips by personal car by
applying the DRS to serve the private travel demand. However, one of the major characteristics
of self-driving vehicles is the ability to drive unoccupied [189], and this aspect was investigated
here. The total VKT by SAVs increased with the increment in the share distribution of SAVs,
where more than 96% of VKT, which SAVs covered in every scenario, were occupied trips.
Table 18 shows all scenarios’ total, occupied, and unoccupied VKT in kilometers.
48.72
49.88
50.89
51.25
2%
4%
5%
0%
1%
2%
3%
4%
5%
6%
47.00
47.50
48.00
48.50
49.00
49.50
50.00
50.50
51.00
51.50
Base Mix-Traffic AV-Focused SAV-Focused
% Change in Velocity
Velocity [km/Hour]
Velocity [KM/Hour] % Change in Velocity
76
Table 18. VKT [km] in the network during AP
VKT [km]
Base
Mix-Traffic
AV-Focused
SAV-Focused
Total VKT
17,228,943
20,510,993
16,923,120
10,991,717
Total VKT by SAV
-
82,673
128,518
466,572
Occupied VKT
-
79,629
124,094
453,275
Unoccupied VKT
-
3,044
4,424
13,297
A statistical analysis of the acquired data was utilized to find the significant differences
between the three proposed future traffic scenarios (Mix-Traffic, AV-Focused, and SAV-
Focused) and the Base scenario for each parameter in the TPPs described above. Friedman and
Wilcoxon signed-rank tests (non-parametric tests) were used to compare future traffic scenarios
to the Base scenario since the data did not fit a normal distribution. The Friedman test with
Bonferroni correction revealed that the distribution of all TPPs among the possible
combinations (i.e., Mix-Traffic – Base, AV-Focused – Base, and SAV-Focused – Base) is not
the same.
For pairwise comparisons, the Wilcoxon signed-rank test was used for all pairings that
had different distributions according to the Friedman test. The results showed that there is a
significant difference for all TPPs in the investigated combinations (p < 0.001). Table 19 shows
the Z-values and effect sizes (r) obtained by dividing the z value by the square root of
observations (N). The effect size increased with the increment in the share distribution of AV
and SAV; moreover, higher values for (r) were noticed in the SAV-Focused scenario.
Exceptions were found in the case of (AV-Focused scenario – Base scenario) for VKT, where
the effect size had a smaller value than in (Mix-Traffic scenario – Base scenario).
Table 19. Wilcoxon signed-rank test results for investigated traffic performance parameters
TPP
Mix-Traffic – Base
AV-Focused – Base
SAV-Focused – Base
Z
r
Z
r
Z
r
Delay
-19.940a
0.11
-52.255a
0.30
-60.033a
0.34
Average Queue
Length
-26.792a
0.15
-48.064a
0.28
-64.769a
0.37
Max Queue Length
-26.359a
0.15
-47.633a
0.27
-64.388a
0.37
Average Density
-20.623a
0.12
-42.973a
0.25
-98.263a
0.56
Volume
-76.876a
0.44
-78.007a
0.45
-112.498a
0.65
Average Velocity
-41.129b
0.24
-56.012b
0.32
-60.978b
0.35
VKT
-69.995b
0.40
-30.744a
0.18
-102.861a
0.59
a. Based on positive ranks (P<0.001); b. Based on negative ranks (P<0.001)
The user’s benefit (i.e., consumer surplus) resulting from the emergence of AVs and
SAVs is displayed in Figure 14. It can be noticed that the emergence of AVs and SAVs caused
a positive change in consumer surplus in all scenarios. The increased consumer surplus is
reasonable considering the lower assumed VOTT for AVs and SAVs in this research. The
highest increment in the consumer surplus occurred in the AV-Focused scenario, and the
positive change in consumer surplus was approximately the same in Mix-Traffic and SAV-
Focused scenarios.
77
Figure 14. Consumer surplus changes for three proposed future scenarios compared to Base scenario
4.5 Conclusion
The introduction of AVs and SAVs into the transportation sector is anticipated to
provide several benefits with regard to the road network. However, the share distribution of AV
and SAV is not yet evident. Therefore, three alternative future traffic scenarios reflecting
various AV and SAV emergence possibilities were devised to explore the potential
consequences of varying AV and SAV penetration rates on the network performance and total
change in consumer surplus in the city of Budapest.
In the modeling procedure for the Base and future traffic scenarios, the forecasted travel
demand for Budapest for the years 2020, 2030, and 2050 was used. The future traffic scenarios
consisted of the inclusion of CCs, AVs, and SAVs for 2030 in the Mix-Traffic scenario and the
replacement of all trips made with CCs by AV and SAV modes for 2050 in the other two
scenarios, including two separate approaches: (1) AVs were considered to be widely utilized as
a private self-driving vehicle in the AV-Focused scenario, and (2) SAVs were assumed to be
largely used in the SAV-Focused scenario. The simulation was carried out using the SBA within
“Visum” software based on an existing and validated traffic model, the “EFM Model”.
The utilization of a professionally designed and calibrated EFM model; deploying SBA
in the network loading process within the assignment; and involving the forecasted travel
demand of the investigated years (2020, 2030, and 2050) in the analysis allow for more stable
results in term of network performance and changes in consumer surplus. In addition, the DRS
was used to model the SAV system, taking into account several essential qualities that are
predicted in SAV structures, such as in-route check and acceptance of other trip requests based
on detour factor, the vehicle power level and recharge, and time constraints for the vehicle to
pick-up a request.
The results show that the advent of AVs and SAVs in the Budapest network will
enhance the TPPs and increase the consumer surplus. The network performance witnessed
additional improvements with a higher replacement rate of CCs by SAVs, where the lowest
queues length, minimum delays, maximum velocity, and lowest VKT took place in the SAV-
Focused scenario, followed by AV-Focused and Mix-Traffic scenarios, respectively. The
improved network performance might induce additional travel demand, which may necessitate
4.76
5.73
4.73
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
Mix-Traffic AV-Focused SAV-Focused
∆CS [Million Euro]
78
applying travel demand management like road pricing [13], [79]; such aspect was not analyzed
here as it falls beyond this research scope. Moreover, this research analyzed the implications of
replacing CCs with AVs and SAVs in alternative future scenarios; however, it could be
interesting for future research to broaden the research area to cover the impact of AVs and
SAVs on the public’s mobility behavior and mode choice.
It is worth mentioning here two related research papers that investigated the impact of
the SAVs emergence on the Budapest network and the impact of AVs emergence on the
Budapest network, considering different penetration rates for each mode separately and
utilizing the forecasted travel demand for the year 2030. The results showed that the
implementation of the SAV system has a positive effect on traffic performance. Based on the
relationships between the modeled SAV demand shares and the network's key performance
parameters in the designed scenarios, the overall network performance showed improvement
along with an increase in the SAV demand share [27]. Similarly, the city of Budapest can
benefit from the advent of AVs to improve traffic performance. The study also illustrated that
using the reaction time factor in SBA for implementing AVs would primarily control (i.e.,
increase) link capacity, which in return would decrease traffic congestion [S11].
79
Related publications to this chapter:
M. Shatanawi, and F. Mészáros, “Implications of the Emergence of Autonomous
Vehicles and Shared Autonomous Vehicles: A Budapest Perspective,” Sustainability 2022, 14,
10952. https://doi.org/10.3390/su141710952.
I. Matalqah, M. Shatanawi, A. Alatawneh, and F. Mészáros, “Impact of Different
Penetration Rates of Shared Autonomous Vehicles on Traffic: Case Study of Budapest,”
Transportation Research Record, p. 03611981221095526, Jun. 2022,
https://doi.org/10.1177/03611981221095526.
A. Alatawneh, M. Shatanawi, and F. Mészáros, “Analysis of the Emergence of
Autonomous Vehicles Using Simulation-based Dynamic Traffic Assignment – The Case of
Budapest,” Periodica Polytechnica Transportation Engineering. [Under Review]
F. Meszaros, M. Shatanawi, and G. A. Ogunkunbi, “Challenges of the Electric Vehicle
Markets in Emerging Economies,” Period. Polytech. Transp. Eng., Feb. 2020,
https://doi.org/10.3311/PPtr.14037.
Thesis III
I justified that replacing conventional vehicles with autonomous and shared
autonomous vehicles would improve the overall network performance and increase the
consumer surplus. A higher replacement rate of conventional vehicles by shared
autonomous vehicles will have a more positive impact on traffic status. A higher
replacement rate of conventional vehicles by autonomous vehicles would generate a
higher consumer surplus. By deploying the dynamic traffic assignment for Budapest
network using a validated macroscopic traffic simulation tool (VISUM), I showed the
impact of self-driving vehicles on Budapest network and consumer surplus for the
proposed future traffic scenarios for the years 2030 and 2050.
80
5 Chapter Five - Implications of Static and Dynamic Road
Pricing Strategies in the Era of Autonomous and Shared
Autonomous Vehicles Using Simulation-Based Dynamic
Traffic Assignment: The Case of Budapest
5.1 Introduction
As mentioned in the introduction section of Chapter three, the advent of AVs and SAVs
is expected to improve network performance and increase accessibility. However, the improved
accessibility will most likely induce more vehicle miles traveled, which necessitates the use of
travel demand management tools like RP. Different RP schemes can be implemented in urban
areas, where these schemes might differ in the way that they levy tolls. These schemes can be
classified into dynamic schemes that depend on the traffic situation (i.e., changing the toll
amount based on the level of congestion) or static schemes where the toll amount does not
change in relation to congestion, see, for example, [190]–[193]. Pricing schemes can also be
further classified considering the toll dependence on the use of the road network into: variable
tolls where the toll increases with increased use of certain roads, as is the case in distance-based
schemes, or fixed tolls where drivers pay a certain toll to cross a specific point regardless of
their use of the road network like area-based scheme [194]. Most of the RP schemes applied in
cities are a simplified form of area-based or cordon-based schemes, and tolls do not vary with
changes in congestion level. However, thanks to modern technology in AVs and SAVs, a more
dynamic RP scheme can be implemented.
This chapter is a continuation of the previous chapter, where I investigate the impact of
different RP strategies on network traffic performance and social welfare in the proposed future
traffic scenarios (Section 3, Chapter 4). The pricing strategies consist of three different RP
schemes, including static-fixed toll (bridge toll scheme), static-variable toll (distance-based
scheme), and dynamic RP (link-based scheme) that were applied to the proposed future traffic
scenarios.
Several studies have explored RP in the presence of AVs and SAVs, and many of them
have used agent-based simulation models. This chapter adopted the SBA of a transport network
model for Budapest (EFM Model) using traffic macroscopic simulation software "Visum" to
address the following research questions:
1. What are the impacts of the applied RP strategies on traffic performance, including
delay, average and maximum queue length, utilization (scaled density), volume, vehicle
kilometers traveled (VKT), and velocity? What are the implications of implementing
the RP strategies in regard to social welfare and generated revenues?
2. How does the performance of pricing strategies differ with different penetration rates
of AVs and SAVs? What is the best RP strategy for each scenario considering network
performance and change in social welfare?
81
It is worth mentioning here that the explanation of the Budapest network model, as well
as the SBA framework for AVs and SAVs, which was built using the Visum software and
utilized in this study, and the development of future traffic scenarios are presented in Chapter
4, Sections 2 and 3, respectively. The remainder of this thesis is structured as follows: Section
2 provides a review of previous research relevant to investigating pricing strategies in the era
of AVs and SAVs. Section 3 elaborates on proposed pricing strategies. After presenting the
results in Section 4, a discussion of the findings can be found, followed by policy implications
in Section 5. Finally, Section 6 highlights the main conclusions of the research.
5.2 Overview of the Research on RP for Self-driving Vehicles
About a century ago, Pigou [69] pioneered the concept of corrective tax and negative
externalities. Since then, many economists have highlighted the effectiveness of RP in
mitigating traffic-related issues [71], [70], [14], [15]. Numerous researchers investigated the
optimal tolling scheme, trying to bridge the gap between the best pricing scheme and the
second-best one [195]–[198]. Similarly, the research papers concerning the impact of AVs and
SAVs on different aspects of mobility, such as network performance, social welfare, and travel
behavior, are expanding quickly [43]. Although there is a vast amount of research concerning
the modeling of RP and self-driving cars, the area of RP considering the emergence of AVs and
SAVs has not yet been thoroughly explored. However, some research papers have provided an
overview of the possible pricing strategies in the era of AVs and SAVs. This section of the
thesis focuses on these studies.
Simoni et al. [9] analyzed the impacts of traditional and advanced pricing strategies in
different future traffic scenarios involving AVs and SAVs on the network of Austin, Texas,
using the agent-based simulation model "MATSim". The main results of the study showed that
the more advanced pricing strategies were more effective in terms of social welfare and
reducing congestion. Tscharaktschiew and Evangelinos [46] studied the impact of RP on AV
choice and vice versa based on the interrelationship among the chosen level of self-driving
vehicles, effective road capacity, traffic flow, and marginal cost. The main findings were that
drivers might opt for conventional vehicles instead of self-driving vehicles if RP is implemented
due to the defects and issues related to AVs (e.g., liability or the hacking of vehicles) and
because of the effect of self-driving vehicles on marginal social cost, which may not necessarily
increase with congestion level, suggesting that RP could be meaningless in the era of AVs and
SAVs. Kaddoura [47] investigated the impact of SAVs and different pricing setups on the
transport system in the city of Berlin, Germany, using the agent-based simulation framework
"MATSim". The main results of the study showed that SAVs would increase the traffic
congestion in the inner city of Berlin, and RP is required to control this increased demand;
however, RP should be applied to SAVs and CCs to be effective and efficient in managing the
demand and to have an impact on the whole transport network, including the traffic conditions,
environment, and social welfare.
Cohen & Cavoli [48] investigated the impact of governmental interventions on traffic
flow and accessibility in the era of AVs and SAVs compared to the laissez-faire approach. They
concluded that a set of well-planned governmental interventions such as banning empty AV
82
trips, providing soft-mobility options, increasing the frequency of public transport, and
applying RP (conventional and smart) would lead to more desirable outcomes compared to a
laissez-faire approach. Moreover, they added that RP has the potential to change traveler
behavior by encouraging them to use transport modes other than personal vehicles, including
AVs, and share their trips with others, thus increasing vehicle occupancy. Hensher [199] echoes
similar sentiments regarding the importance of RP in the era of AVs and SAVs to tackle the
expected traffic congestion caused by greater accessibility. Bösch et al. [49] explored various
policy combinations in the era of AVs and SAVs in the city of Zug, Switzerland, using
MATSim software. The results showed that a high penetration rate of AVs would reduce travel
time but increase VKT. The tested transport measures, including pricing private and public
transport, as well as organizing the AVs and SAVs service like including or excluding ride-
sharing, revealed that the effect of AVs and SAVs on traffic is complex and requires careful
policy crafting. The parking behavior of AVs and SAVs was investigated by Millard-Ball [200],
and the results of the microsimulation model showed that AVs and SAVs might double the
number of trips to, from, and inside urban centers, or even more than doubling it, and 90% of
this extra travel is related to searching for parking based on the cheapest option, which involves
sending cars back home, searching for free on-street parking, or just cruising in the city if it is
cheaper than parking. The author suggested the solution of implementing RP in urban cores in
a way that prevents AVs cruising to avoid parking fees by applying two toll schemes: time-
based and distance-based pricing schemes.
The possible impact of four RP strategies (Distance-based, Origin-based, Destination-
based pricing schemes, and Combination pricing) on shared autonomous electric vehicles,
among other modes (i.e., CC and bus service) using an agent-based model was explored by
Chen & Kockelman [29]. The simulation results projected that electrical SAVs would possess
14% to 39% of the market share, as with higher electric SAVs market penetration, more CCs
will be replaced by them. Moreover, the impact of the various RP strategies differs based on
the operator's goal and the selected pricing scheme; therefore, a trade-off is required to reach
the optimal solution for private users and public operators of electric SAVs. Sharon et al. [21]
proposed an adaptive RP scheme for AVs that requires only the current and free-flow travel
time, which can be easily obtained due to the advanced technology used by AVs and SAVs to
calculate a toll value that reduces congestion and increases social welfare compared to the no-
toll scenario. However, the study did not investigate the system for SAVs. This gap was covered
by studying the performance of SAVs with a dynamic ride-sharing option in conjunction with
RP [50]. The results of the study revealed the benefits of dynamic ride sharing in reducing
congestion and put emphasis on the role of RP in enhancing fleet performance, as it moderates
the vehicle miles traveled and increases the demand for SAVs and revenues. Another study [51]
investigated the impact of adaptive tolls that vary according to the congestion level of
automated vehicles in a Mix-Traffic flow scenario (i.e., automated vehicles along with CCs).
The results showed that dynamic RP increases the speed on tolled links compared to non-tolled
ones, with a tendency for users with a higher value of travel time to use tolled links. Moreover,
applying tolls along with more penetration of automated vehicles would benefit most users and
improve traffic conditions.
83
5.3 Pricing Strategies
Three pricing strategies that represent different types of tolls were examined in this
study to determine their impact on the Budapest network performance parameters and the
change in social welfare in the era of AVs and SAVs. The first is "Bridge Toll", which uses the
flat-rate toll concept. The second is the "Distance-based" scheme that charges users for the
distance traveled in the designated area. These strategies are static (i.e., traffic independent), as
they apply the tolls regardless of network congestion conditions. In contrast, the third pricing
strategy, "Link-based scheme", is dynamic (i.e., traffic dependent) where users will be charged
a toll if there is congestion in the network. A link-based scheme was applied where users need
to pay for using congested links on the network.
5.3.1 Static RP Strategies
The tolls are levied in static pricing schemes regardless of network conditions. These
are well-known strategies cited in academic research and in practice and have been applied in
different forms across the world, for instance, in London, Stockholm, and Milan [115], [201]–
[203], [192]. Bridge Tolls have been applied in this study since there are several bridges
crossing the Danube River and linking the two sides of the capital Budapest (Buda and Pest);
moreover, these bridges are the mainly used links between the two parts. To the best of my
knowledge, there are no prior studies that have investigated the impact of implementing Bridge
tolls on the Budapest network. A simple flat-rate toll of 400 HUF/cross is applied for seven of
them, where all the seven bridges considered for tolling are centrally located in the city. The
eighth bridge (Megyeri Bridge), in the extreme north, was excluded from tolls because it is
distant from the inner city and offers a substitute route for users who do not want to pay the toll.
The bridges considered for tolling, from north to south, are Árpád Bridge, Margaret Bridge,
Széchenyi Chain Bridge, Elisabeth Bridge, Liberty Bridge (Szabadság Bridge), Petőfi Bridge,
and Rákóczi Bridge, as shown in Figure 15. The value of the toll was selected to be close to the
price of public transport tickets and based on the Budapest Mobility Plan (an official plan
accepted by the municipality of Budapest), which recommended the value of 400 HUF/day for
cordon crossing [204]. Although it is needed to test different toll combinations at different times
(peak and off-peak) to ascertain the impact on network performance and social welfare, this
pricing scheme is limited here to investigate the impact of one proposed toll at the morning
peak period from 7:00 to 8:00, which represents the highest demand share during the day and
the worst-case scenario.
84
Figure 15. Budapest's planned toll bridges
The first step in designing the distance-based scheme was to define an area that covers
the inner city of Budapest, and all vehicles traveling inside the designated area will be charged
a toll amount based on the distance traveled inside this area. Figure 16 shows the selected area
for implementing the distance-based scheme. The size of the designated distance-based tolling
area is 33 KM2; it covers 180 zones and comprises 4242 links. This area was selected for
practical reasons, as the public would not find it acceptable to apply tolls on the whole network,
as they may consider it unfair [72, pp. 568, 578]. Moreover, traffic congestion usually occurs
in the city center during peak hours, and the selected area covers the most congested zones in
Budapest, containing 73% of the daily trips. The distance-based toll value () was calculated
based on VOTT (HUF/hour), the average delay in travel time (hour/AP), and the average car
mileage (KM/AP), as shown in Equation 12. The tolls were applied during the time interval
7:00 AM to 8:00 AM. The values of delay and average car mileage were extracted from EFM
original model without any amendments.
85
Figure 16. Distance-based tolling scheme area
5.3.2 Dynamic RP Strategy
Here, the link-based scheme applied variable tolls on each link in the network according
to the percentage of queue lengths on each link. At first, the average lengths of all links in the
network were calculated (0.475 KM). All links with lengths greater than the average lengths of
the links were considered for tolling, while the links with a shorter length than the average
lengths of links were eliminated. This criterion was set for practical reasons, as the toll value
of the shorter links would be too small and insignificant. The total number of considered links
for tolling was 7250 in all scenarios and is depicted in Figure 17. In the next step, the congested
links out of the 7250 links, which were defined by the presence of a queue on the link during
the simulation, were subjected to toll. Other links which did not have a queue were eliminated
as they were not congested. Then, the percentage of queue lengths on each link was calculated
by dividing the queue length on the link by the link's length to define the congestion level on
each link. The applied toll value on each link was calculated by multiplying the value of toll in
Equation 12 (45.8 HUF/km) with the link's length; moreover, once the percentage of queue
length on the link reached 60%, the toll amount increased by 20% with each 10% increment of
queue length on each link, as illustrated in Table 20.
Table 20. Link-based Scheme toll criteria and calculation method
Number
Criteria
Toll Value Calculation Method
1
0.0 < % queue length on each link < 0.6
100% 45.8 link’s length
2
0.6 ≤ % queue length on each link < 0.7
120% 45.8 link’s length
3
0.7 ≤ % queue length on each link < 0.8
140% 45.8 link’s length
4
0.8 ≤ % queue length on each link < 0.9
160% 45.8 link’s length
5
0.9 ≤ % queue length on each link < 1.0
180% 45.8 link’s length
6
% queue length on each link ≥ 1.0
200% 45.8 link’s length
86
In this way, the toll values in the link-based scheme varied with the change in congestion
levels inside the network in real-time. However, the increment in toll values associated with the
increased percentage of queue lengths on links requires more in-depth analysis, formulation,
and validation, which can be further analyzed in future work on this subject. For example,
applying an exponential or polynomial approach could be more beneficial by punishing the
bigger queues with higher toll values.
Figure 17. Selected links for tolling in the Link-based Scheme
The link-based scheme calculated the toll value in each analysis time interval (ATI)
which was set to 10 minutes during the AP [7:00 AM to 8:00 AM] and the termination condition
that guarantees all vehicles have exited the network after the last iteration [8:00 AM to 9:00
AM]. The number of tolled links and the average toll values during each ATI for each scenario
are represented in Figure 18. The number of tolled links kept increasing in all scenarios until
8:00 AM when it started to decrease; a similar pattern could be seen for average toll values
(HUF/vehicle/link) in the Mix-Traffic and AV-Focused scenarios, but in SAV-Focused
scenario, it kept increasing until 8:10 AM, then began decreasing. The highest number of total
tolled links and the average of toll values from 7:00 AM to 9:00 AM happened in the AV-
Focused scenario with 10758 tolled links and 7.74 HUF/veh/link, followed by the Mix-Traffic
scenario at 7449 tolled links and 5.51 HUF/veh/link; the lowest was seen in the SAV-Focused
scenario at 5783 tolled links and 4.68 HUF/veh/link.
87
Figure 18. Tolled links and average toll value at each ATI for all scenarios in Link-based Scheme
5.4 Results
The results presented in this section illustrate the impact of the three applied RP
strategies in every scenario on TPP, including delay, queue lengths, and volume. This section
also compares the generated revenues from the pricing strategies and shows the change in
consumer surplus and social welfare as a result of their application.
5.4.1 Changes in TPP
The changes in TPPs were investigated to assess the impact of different RP strategies
on network performance. The analyzed TPPs are delay, average and maximum queue length,
utilization, volume, VKT, and velocity. The following paragraphs provide an explanation for
each of these parameters.
Delay is calculated in the SBA from the travel time in a network without volume (t0)
and the average travel times during the AP, taking volume into consideration (tCur). Figure 19
shows the percentage changes in delay due to applying RP strategies for each scenario. The
percentage change in delay illustrates that the pricing strategies' effect differs according to
different penetration rates of AVs and SAVs. All pricing strategies have a positive influence in
reducing the delay in the AV-Focused scenario, whereas the effect in the SAV-Focused
scenario is small. Surprisingly, in the Mixed-Traffic scenario, the distance-based scheme
caused a large increment in the total delay in the network, while the bridge toll increased it by
3% and link-based schemes reduced it by 2%.
88
Figure 19. Percentage change of traffic delay for all scenarios according to the RP scheme
SBA queue length attributes (lane average and lane max) are two of the SBA-related
outputs, which show the average and maximum length of the queue on the link edges assigned
to lanes at each ATI. It is calculated by dividing the waiting times of vehicles at the end of a
lane by the ATI and then multiplying this fraction of time with the average aggregated vehicle
length during the ATI. The effect of the pricing strategies on average and maximum queue
lengths differs in each scenario. Considering that the summation of average queues accumulated
on each link in the network for every case at 8:00 AM shows that in the case of the Mixed-
Traffic scenario, the distance-based and link-based schemes increased the average queue length
by 37% and 35%, respectively, while the bridge toll has a small effect. On the contrary, in the
AV-Focused scenario, queues disappeared when applying bridge and distance-based schemes
and decreased by 26% in the case of the link-based scheme. Furthermore, the pricing strategies
have different effects on average queue length in the SAV-Focused scenario, where the bridge
toll scheme increased it by 25%, the link-based scheme reduced it by 8%, and the distance-
based scheme had almost no effect.
The SBA Max queue length shows that the maximum queue length was accumulated
on each link at each ATI. Figure 20 depicts the summation of the average queue length on the
left y-axis and the maximum of max queue for each case in the whole network on the right y-
axis at 8:00 AM. The maximum of max queue length followed a similar pattern to average
queue length, where it increased when applying pricing strategies in Mix-Traffic flow,
decreased in the case of the AV-Focused scenario, and fluctuated in the SAV-Focused scenario.
In a wider perspective, the percentage change in the summation of max queue lengths on all
links in the network yields again a similar pattern of the summation of the average queue length
with slightly different percentages. For the Mix-Traffic scenario, the distance-based and link-
based schemes increased the summation of max queue length by 34% and 7%, respectively,
while the bridge tolling decreased it by 2%. For the AV-Focused scenario, queues vanished
when applying bridge tolls and distance-based schemes, and they decreased by 19% in the case
of the link-based scheme. For the SAV-Focused scenario, the bridge toll scheme increased the
3%
-18%
1%
52%
-13%
1%
-2%
-8%
-1%
-30%
-20%
-10%
0%
10%
20%
30%
40%
50%
60%
Mix-Traffic AV-Focused SAV-Focused
% Changes in Delay
Bridge Toll
Distance-based Scheme
Link-based Scheme
89
summation of max queues length in the whole network by 19%, the link-based scheme
decreased it by 7%, and the distance-based scheme had almost no effect.
Figure 20. SBA queue length (summation of average and maximum of max) @ 8:00 AM for all scenarios
according to the RP schemes, where NT: No Toll (Base case scenario), BT: Bridge Toll, DT: Distance-based
Scheme, LT: Link-based Scheme
SBA utilization is the output attribute extracted during the network loading process
within the assignment. It refers to a scaled density which provides an illustration of the
utilization of the link capacity according to the fundamental diagram, which relates volume and
density values. A visualization that compares the SBA utilization of the three pricing strategies
to the Base Case scenario (No Toll) for each scenario is represented in Figure 21. The pricing
strategies affect the utilization differently in every scenario. The bridge toll scheme had its
maximum impact on the bridges and their surroundings; moreover, the traffic behaves
differently when using the bridge toll in each of the investigated scenarios. In the Mix-Traffic
scenario, utilization was improved on bridges and their surroundings except for the Chain
Bridge and its surroundings, where utilization was worsened. In the AV-Focused scenario, the
utilization improved for all bridges but worsened in the city center. The impact of the bridge
toll on utilization is the lowest in the SAV-Focused scenario with slightly better utilization in
the network.
The distance-based scheme followed a similar pattern of affecting the utilization across
the three scenarios, where the utilization was worsened on the border and outside of the pricing
area but improved inside it. This behavior can be interpreted as the commuters trying to
circumvent crossing the pricing area by choosing alternative routes around it to avoid paying
tolls. The link-based scheme's impact on utilization was distributed in the whole network
following the nature of the pricing scheme itself. In general, the link-based scheme worsened
the utilization in the AV-Focused scenario and to a lower extent in the SAV-Focused scenario,
while in the Mix-Traffic scenario, improved and worsened utilization was identified throughout
the network.
13.00 13.08
17.88 17.60
4.01
2.98
0.44 0.55 0.45 0.41
280.1
384.5 384.8
645.4
284.31
149.80
23.6
80.8
18.6 20.9
0.0
100.0
200.0
300.0
400.0
500.0
600.0
700.0
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
18.00
20.00
NT BT DT LT NT BT DT LT NT BT DT LT
Mix-Traffic AV-Focused SAV-Focused
Maximum of Max Queue length [M]
Summation of Average Queue length [KM]
Summation of average queue
length
Maximum of max queue length
90
Mix-Traffic
AV-Focused
SAV-Focused
Bridge Toll – No toll
Distance-based Toll – No toll
Link-based Toll – No toll
Figure 21. The change in SBA utilization after applying RP strategies compared to the "No Toll Case" for all
scenario
As this research focuses on analyzing the impact of the pricing strategies in the proposed
future traffic scenarios considering the private transport mode with assuming a constant modal
share, the traveler would change their route to reach their destinations, not their mode choice.
As a result, the volume, VKT, and velocity have not or only slightly changed on the network
level; nevertheless, there was a change in their values on the links level. Table 21 represents
the change in volume, VKT, and velocity on the link level due to applying the pricing strategies.
For each strategy, two cases (Negative and Positive) were investigated. The negative
means that values of volume, VKT, and velocity had greater values when the No Toll case was
applied (Base) than when tolls were applied, and vice versa for the positive. Furthermore, the
table includes the number of links (Counts) that have been affected by the pricing strategies and
the percentage change (% change) of the three above-mentioned parameters for the two
investigated cases in each scenario. At first, it can be seen that the increase in one of the
parameters on certain links is offset by a decrease of approximately the same amount on other
links within each scenario, and this leads to a very slight change at the network level. Moreover,
the link-based scheme had a relatively greater effect on volume, VKT, and velocity than the
other two pricing strategies in the case of Mix-Traffic and AV-Focused scenarios, unlike the
case of the SAV-Focused scenario, in which its impact on volume and VKT was relatively less
than bridge toll and distance-based schemes.
91
Table 21. The impact of the RP strategies on volume, VKT, and velocity on the link's level
Mix-Traffic
AV-Focused
SAV-Focused
Traffic Performance
Parameters
Negative
Positive
Negative
Positive
Negative
Positive
Count
(% change)
Count
(% change)
Count
(% change)
Count
(% change)
Count
(% change)
Count
(% change)
Volume
BT - NT
5652
(-2.8%)
5203
(2.7%)
5061
(-2.5%)
6027
(2.4%)
8370
(-3.9%)
8282
(3.2%)
DT - NT
4684
(-3.6%)
6180
(3.3%)
4982
(-2.8%)
6107
(2.3%)
7332
(-4.6%)
9316
(4.0%)
LT - NT
5114
(-3.9%)
5761
(4.1%)
4885
(-2.5%)
6304
(3.0%)
7522
(-2.2%)
9366
(2.4%)
VKT
BT - NT
8973
(-4.2%)
8470
(4.1%)
7885
(-3.4%)
8990
(3.7%)
8306
(-3.1%)
8288
(3.7%)
DT - NT
7836
(-5.5%)
9778
(4.9%)
7794
(-3.4%)
9117
(3.2%)
7373
(-3.2%)
9310
(3.3%)
LT - NT
8087
(-6.1%)
9586
(6.0%)
7737
(-4.4%)
9649
(4.0%)
7514
(-2.7%)
9390
(2.8%)
Velocity
BT - NT
4593
(-4.3%)
4632
(3.3%)
4178
(-2.0%)
4273
(1.7%)
3454
(-1.1%)
3419
(1.0%)
DT - NT
4907
(-5.4%)
4822
(3.3%)
4304
(-2.4%)
4301
(1.6%)
3707
(-1.5%)
3747
(1.0%)
LT - NT
4868
(-5.3%)
4956
(3.2%)
4607
(-2.9%)
4252
(1.7%)
3781
(-2.0%)
3721
(1.1%)
A further investigation of the three parameters mentioned above (volume, VKT, and
velocity) was carried out by performing a statistical analysis of the obtained data to identify
significant differences between the pricing strategies and the No Toll (Base Case Scenario) for
each parameter. As the data did not fit a normal distribution, Friedman and Wilcoxon signed-
rank tests (non-parametric tests) were applied to compare the pricing strategies — Bridge Toll
(BT), Distance-based Scheme (DT), Linked-based Scheme (LT) — with the No Toll applied
case (NT) in every scenario. The Friedman test with Bonferroni correction showed that the
distribution of the volume and VKT among the possible combinations (i.e., BT-NT, DT-NT,
LT-NT) in the three scenarios is not the same, except in the case of the SAV-Focused scenario
where the volume and VKT had a similar distribution in the comparison of BT-NT. In regard
to velocity, there was a significant difference in distribution only in the case of (LT-NT) in the
Mix-traffic and AV-Focused scenarios, and all other comparisons had the same distribution.
The Wilcoxon signed-rank test was applied for pairwise comparison for all
combinations which had different distributions according to the Friedman test. The results
showed that there is a significant difference for the investigated variables in all possible
combinations (p < 0.001) for volume and VKT and (p < 0.01) for velocity. Table 22 shows the
Z-values and the statistical significance (p), noting that the effect size (r) is small in all cases,
which can be explained by the small changes in a limited number of links compared to the
whole network size. The empty cells in Table 22 reflect the fact that the Wilcoxon signed-rank
test was not applied to these cases where the Friedman test revealed that the distribution of the
data is the same.
92
Table 22. Wilcoxon signed-rank test results for tested variables
TPP
Mix-Traffic
AV-Focused
SAV-Focused
z
P
z
p
z
p
Volume
BT – NT
-8.820a
<0.001
-3.537b
<0.001
DT – NT
-10.32b
<0.001
-5.594b
<0.001
-9.670b
<0.001
LT – NT
-7.230b
<0.001
-13.71b
<0.001
-15.915b
<0.001
VKT
BT – NT
-6.827a
<0.001
-4.108b
<0.001
DT – NT
-11.11b
<0.001
-6.034b
<0.001
-10.238b
<0.001
LT – NT
-7.797b
<0.001
-12.93b
<0.001
-15.994b
<0.001
Velocity
LT – NT
-2.875b
<0.01
-2.957a
<0.01
a: Based on positive ranks; b: Based on negative ranks
A further illustration of the impact of the pricing strategies on volume, VKT, and
velocity is presented in Figure 22, which visualizes the effect of the bridge toll on volume, the
distance-based scheme on VKT, and the link-based scheme on velocity in each scenario. The
bridge toll decreased the traffic volume on all bridges in all scenarios, while the distance-based
scheme has a positive impact on VKT inside the charging area by reducing it and a negative
impact outside the charging area and in its surroundings as VKT was increased in all scenarios.
The last row in the figure shows how the velocity fluctuates due to applying the link-based
scheme, increasing on certain links and decreasing on others by a similar amount in each
scenario. However, the link-based scheme impact is higher in Mix-Traffic and AV-Focused
scenarios than in SAV-Focused scenario.
Mix-Traffic
AV-Focused
SAV-Focused
Volume (Bridge Toll – No Toll)
VKT (Distance-based Toll – No Toll)
Velocity (Link-based Toll – No Toll)
Figure 22. The change in volume, VKT, and velocity after applying RP strategies compared to the "No Toll
Case"
93
5.4.2 Generated Revenues, Consumer Surplus, and Social Welfare
I calculated the consumer surplus here using Equations 9 and 10, relying on the same
concept presented in Section 4.2.2.3 (i.e., RoH) to provide an approximation of how the applied
RP strategies would affect the consumer surplus. Furthermore, I estimated the total change in
social welfare by adding the generated revenues from implementing each pricing strategy (i.e.,
the summation of collected tolls) to the change in consumer surplus (∆CS) as a result of
applying the corresponding pricing strategy [9].
The summation of all gathered tolls (i.e., generated revenues) differed by the applied
pricing strategy; however, the generated revenues followed a similar pattern in every scenario.
The link-based scheme generated maximum revenues in all scenarios (ranging from 4.5 to 6
times higher than distance-based and bridge toll schemes), followed by the distance-based
scheme (see Table 23). These results make sense considering the size of the tolling area of each
tolling scheme: the link-based scheme was applied to the whole network, while the distance-
based scheme was implemented inside the city center, and the bridge toll at bridges only. It can
be noticed that the different penetration rates affect the generated revenues as well; in the Mix-
Traffic scenario, the generated revenues were the maximum, but they decreased in the AV-
Focused scenario by 22%, 5%, and 15% for BT, DT, and LT, respectively. A further reduction
occurred in the SAV-Focused scenario, nearly by half compared to the other two scenarios.
The user's benefit (consumer surplus, CS) is calculated for each pricing strategy in every
scenario and displayed in Table 23. It can be noticed that social welfare is reduced without
including the tolls. The lowest reduction occurred when applying the distance-based scheme in
Mix-Traffic and AV-Focused scenarios, while in the SAV-Focused scenario, the bridge toll
produced the lowest reduction in consumer surplus. Unlike revenues generated from applying
the link-based scheme, it caused the highest reduction in consumer surplus in all scenarios.
Table 23. Consumer Surplus and Generated Revenues [Million HUF]
Mix-Traffic
AV-Focused
SAV-Focused
∆CS
Revenues
∆CS
Revenues
∆CS
Revenues
Bridge Toll
-7.743
12.390
-11.599
9.637
-6.587
5.283
Distance-based Scheme
-7.219
12.468
-9.744
11.837
-7.405
6.999
Link-based Scheme
-64.205
66.425
-53.884
56.132
-31.984
31.656
The total social welfare change (the sum of generated revenues and ∆CS for each pricing
strategy in every scenario) is depicted in Figure 23. In the Mix-Traffic scenario, all pricing
strategies produced a positive change in social welfare. In terms of producing positive social
welfare, the best scenario was the distance-based scheme, followed by the bridge toll and link-
based schemes, respectively. Different impacts of the pricing strategies occurred in the other
two traffic scenarios. In the AV-Focused scenario, the link-based scheme produced the
maximum positive change in social welfare, followed by the distance-based scheme. On the
contrary, the bridge toll reduced social welfare by 1.96 million Hungarian forints. All pricing
strategies caused a reduction in social welfare in the case of the SAV-Focused scenario, with
the lowest reduction resulting from the link-based scheme, then distance-based and bridge toll
schemes, respectively. The results of welfare change in this research are in line with previous
research [9] in terms of reducing the consumer surplus when applying all pricing strategies and
94
in terms of the finding that dynamic pricing schemes outperform static ones for AV-Focused
and SAV-Focused scenarios with reference to the change in social welfare.
Figure 23. Welfare changes for RP strategies for each scenario
5.5 Discussion and Policy Implications
The results showed that the impact of the same pricing strategies on TPP and social
welfare fluctuated with the change in AV and SAV penetration rates. For example, it can be
noticed that the RP strategies generally improved the TPP in the AV-Focused scenario,
worsened them in the Mix-Traffic scenario, and showed the lowest impact in the SAV-Focused
scenario. While the change in social welfare showed that the implementation of a dynamic
pricing strategy in the case of AV-Focused and SAV-Focused scenarios is better in terms of
gained social benefits, on the contrary, the classical pricing strategies outperform dynamic ones
in the Mix-Traffic scenario. Therefore, it is recommended that policymakers should adjust
proposed RP strategies to suit the penetration rates of AVs and SAVs in the market, and the use
of dynamic pricing strategies is recommended when AVs and SAVs have a high share of the
transport market. Accordingly, the remainder of this section discusses the impact of the pricing
strategies on TPP and change in social welfare for every scenario and its related policy
implications separately.
The application of the three pricing strategies in the Mix-Traffic scenario had a
relatively minor effect on the TPP in the case of the bridge toll and worsened them when
distance-based and link-based schemes were applied. The revenues generated from applying
the link-based scheme were five times more than revenues generated by the bridge toll or
distance-based schemes; conversely, the link-based scheme reduced the consumer surplus by
more than four times than the other two strategies. However, the positive change in social
welfare in the distance-based and bridge schemes is higher than in the link-based scheme.
Accordingly, considering the change in social welfare, it is advised to use static RP strategies
in the presence of CC as a dominant private transport mode (i.e., the bridge toll and distance-
based schemes).
4.648
-1.962
-1.304
5.249
2.093
-0.406
2.220 2.248
-0.328
-3.000
-2.000
-1.000
0.000
1.000
2.000
3.000
4.000
5.000
6.000
Mix-Traffic AV-Focused SAV-Focused
Total Change in Social Welfare
[Million HUF]
Bridge Toll
Distance-based Scheme
Link-based Scheme
95
The network performance was improved when applying the three pricing strategies in
the AV-Focused scenario, and better improvements took place when applying the bridge toll
and distance-based schemes compared to the link-based scheme. However, the link-based
scheme generated 5.8 and 4.7 times more revenues than the bridge toll and the distance-based
schemes, respectively. Consequently, it produced a maximum positive gain in social welfare,
followed by the distance-based scheme; the bridge toll caused a loss in terms of social welfare.
Therefore, the use of dynamic pricing strategies like link-based schemes or static-variable ones
(i.e., the distance-based scheme) is preferable in such a scenario.
The impact of the pricing strategies on network performance is relatively small in the
case of the SAV-Focused scenario, and the link-based scheme yielded slightly better
performance than the bridge toll and distance-based schemes. Similar to the previous two
scenarios, the generated revenues of the link-based scheme have exceeded the gathered tolls of
the bridge toll and distance-based schemes by a factor of 6 and 4.5, respectively. Although the
∆CS and generated revenues were reduced in the SAV-Focused scenario compared to the other
two scenarios, the reduction in the generated revenues was greater than the reduction in
consumer surplus. Consequently, the change in social welfare was negative for all pricing
strategies. However, these results would be more precise if they included the presumable gains
in terms of reduced noise, air pollution, and collisions which are beyond the scope of this
research. As in the AV-Focused scenario, it is recommended that the link-based scheme should
be implemented if the SAVs are widely available and used or to consider different ways for
transport demand management as the RP strategies’ impact on traffic and social welfare was
minimal.
Finally, it is worth mentioning that the impact of the distance-based scheme differed
inside and outside the charging zone, and therefore it is essential to define charging zone borders
in the case that cordon pricing is adopted by carefully investigating the expected impacts of the
proposed strategies on the border and surrounding area of the charging zone as well.
5.6 Conclusion
This study investigated the possible impacts of different static and dynamic RP
strategies on the network performance and total change in social welfare in the presence of self-
driving vehicles in the city of Budapest. The advent of AVs and SAVs in the transportation
market is expected to bring many benefits in terms of network performance and increasing
accessibility; however, the impact of this emergence on congestion is still vague as the
improved accessibility will most likely lead to more trips and VKT in the network. Similarly,
the share distribution of the penetration rates of AVs and SAVs is not yet clear. Therefore, three
alternative future traffic scenarios representing different possibilities of AV and SAV evolution
were developed, alongside three RP strategies that render possible solutions to internalize the
externalities of traffic congestion in the future.
The forecasted travel demand of Budapest for the years 2030 and 2050 was adopted in
the simulation process for future traffic scenarios. These future scenarios were characterized by
the presence of CCs, AVs, and SAVs for the year 2030 in the first scenario, and replacing all
CCs by AV and SAV modes for the year 2050, including two distinct approaches: (1) in the
96
second scenario, AVs were assumed to be highly adopted, (2) in the third scenario, SAVs were
assumed to be widely used. The network loading process within the assignment took place
using dynamic traffic assignment of a ready transport network model for Budapest (the EFM
Model) using the traffic macroscopic simulation software "Visum". The use of a professionally
designed and calibrated model which represents the transportation network of Budapest,
applying the dynamic traffic assignment in the simulation process, and considering the
projected demands of the respective years (2030 and 2050) for the proposed future traffic
scenarios allow for more realistic predictions of the possible changes in the Budapest network
and social welfare due to applying the pricing strategies in real-time. This study covers different
RP strategies (static and dynamic), which provide a helpful comparison of the possible
operational solutions for traffic congestion in alternative future traffic scenarios. Furthermore,
this study involved the VOTT for different transport modes and DRS for the modeling of the
SAV system, considering several important characteristics that are expected in SAV systems,
such as time constraints for the vehicle to pick up a request; in-route check and acceptance of
other trip requests based on detour factor; and the vehicle power level and recharge.
As the results revealed, the pricing strategies influence traffic and social welfare
differently according to the composition and distribution of transport modes, and area-specific
pricing strategies are recommended to find the optimal option. However, it can be stated that
the dynamic pricing strategies (e.g., link-based scheme) are preferred in the presence of AV
and SAV as dominant transport modes, while static pricing strategies are a better option when
CC has the highest share of private transport modes. In all the scenarios, the link-based scheme
generated the maximum revenues (i.e., gathered tolls) along with being the best pricing strategy
in terms of change of social welfare for AV-Focused and SAV-Focused scenarios and in terms
of producing a positive change in the case of the Mix-Traffic scenario. Therefore, if the
authority's objective of applying the RP strategy is to generate revenues, then a link-based
scheme can be an optimal solution.
This research opens new doorways in the field of transportation by evaluating different
RP strategies in the era of AVs and SAVs; however, it faces some limitations. The toll values
were chosen in this research after thorough deliberation; however, the toll amount can still cause
research bias and assuming ranges of tolls based on respondents' opinion or a toll value
corresponding to marginal external cost caused by the drivers could be better ways, but this
could exponentially increase the research scope. This factor has been minimized by the
selection of rational toll values. As mentioned earlier, this research focuses on the changes in
network performance and social welfare due to the implementation of RP strategies that
consider private transport modes exclusively; consequently, it is needed to extend this research
to include the effect of pricing strategies on mode choice and people's mobility behavior. It
would be interesting to consider RP acceptability, as it is a key determinant of the successful
implementation of an RP scheme [13], [79] in the modeling process through distributing a
questionnaire (e.g., stated preference experiment) that investigates the willingness to pay for
adopting AVs and SAVs, as well as the acceptable toll value for the public along with other
parameters which can be inserted into the modeling process.
97
Related publications to this chapter:
M. Shatanawi, A. Alatawneh, and F. Mészáros, “Implications of Static and Dynamic
Road Pricing Strategies in the Era of Autonomous and Shared Autonomous Vehicles Using
Simulation-Based Dynamic Traffic Assignment: The Case Of Budapest,” 2022, Research in
Transportation Economics, 101231. https://doi.org/10.1016/j.retrec.2022.101231
Thesis IV
I found that implementing the dynamic pricing strategy (i.e., Link-based Scheme) in
the presence of autonomous and shared autonomous vehicles as the dominant
transport modes shows better outcomes than static pricing strategies. Conversely, the
static pricing strategies (i.e., Bridge Toll and Distance-based Schemes) outperform the
dynamic ones when conventional vehicles have the highest share of private transport
modes. By deploying the simulation-based dynamic traffic assignment for Budapest
network using a validated macroscopic traffic simulation tool (VISUM), I showed the
impact of three road pricing strategies (two static and one dynamic) on network
performance and social welfare in the era of self-driving vehicles for three alternative
future traffic scenarios for the years 2030 and 2050.
98
6 Chapter Six - The Summary of Scientific Results and Future
Research
Self-driving vehicles are associated with many advantages to the transportation market
and road users, such as better utilization of travel time. However, they may also increase the
number of trips and traveled miles on roads due to improved accessibility, thus aggravating
congestion. RP is a possible solution for mitigating traffic-related problems like congestion. On
the one hand, the public usually resents RP, which may hinder its introduction. On the other
hand, the advent of AVs and SAVs introduces the opportunity to implement more advance and
dynamic RP strategies. This dissertation investigates the possible approaches to applying RP
successfully and efficiently in the light of the emergence of AVs & SAVs. The following
paragraphs sheds light on the new scientific findings, the practical use of the theses and outlook,
and potential scope for future research work on this subject.
This dissertation bridges some research gaps and contributes to the literature on
transport economics by answering several research questions concerning the application of RP
in the era of AVs and SAVs. Moreover, the dissertation research topics are wide and
comprehensive by covering both aspects of implementing RP: the theoretical one (i.e.,
acceptability of RP) and the practical one (i.e., modeling of different RP strategies). For
example, these are some of the questions which were answered within this dissertation:
• How will the public perceive the RP with the emergence of AVs and SAVs?
• How will the adoption of AVs and SAVs be affected in the presence of RP?
• What are the implications of AVs and SAVs advent?
• Which is the optimal RP strategy in the era of AVs and SAVs?
Furthermore, the dissertation deals with the impact of AVs and SAVs on a transport
network model and the application of different potential static and dynamic RP strategies.
Deploying the SBA in the simulation process will contribute significantly to modeling projects
and related research and can have a hand in crafting traditional and advanced RP policies using
modeling software like Visum. Some of the strengths of the work are that the research provides
a methodological framework for deploying AVs and SAVs using SBA, including, but not
limited to, several essential characteristics that are expected to play important roles in
automated systems, such as vehicle reaction time and headway, vehicle-to-infrastructure
communications, time constraints for the shared vehicle to pick-up a request, in-route check
and acceptance of other trip requests based on determined factors, and vehicle power level. The
use of real-time simulation allowed for the application of advanced RP schemes that are
anticipated to be implemented in the presence of driverless vehicles, thanks to the advanced
technology which such vehicles possess. Moreover, the study illustrated the methods used for
the model calibration and demonstrated the use of the random seed, which can be useful in other
applications. Following is a short summary of the main scientific findings, the practical use of
the theses, and the scope of future research works for enhancing RP acceptability and applying
RP strategies in the era of AVs and SAVs.
99
• I distributed ten surveys in eight different countries to investigate the factors that might
influence the acceptability of RP and the adoption of AVs and SAVs. I succeeded in
collecting 2136 completed and valid responses from Hungary, Jordan, Azerbaijan, Syria,
Mongolia, Ukraine, Tunis, and Brazil. The selected countries in theses one and two, on
the one hand, have different economic conditions, and as Nordhoff et al. [119] showed
that the economic level of a given country plays a role in influencing the adoption of
automated vehicles through GDP per capita. The countries selected in this research
illustrate its breadth by analyzing the research impacts in countries that represent
different economic conditions; for example, Jordan and Brazil have developing
economies, while Ukraine has an economy in transition, and Hungary has a developed
economy [120]. On the other hand, as the problem of increasing demand for motorized
transport modes due to the advent of AVs and SAVs is expected to be a global problem,
I studied the RP acceptability issue in a global framework. The collected data from all
countries were analyzed using various econometric models (e.g., factor analysis, MNL,
MLR, and descriptive statistics).
o In thesis one, I distributed a survey in five capitals to determine the factors that
influence RP acceptability based on well-known methodologies. I measured the
current level of RP acceptability in the surveyed cities, identified the factors
that affect RP acceptability in the studied cities, and provided a broader scope
for testing the underlying model in different environments and societies. I found
a significant positive relationship between the acceptability of road pricing and
the factors "scheme's effectiveness", "scheme's awareness", and "individual
responsibility for traffic-related problems". Moreover, the acceptability of road
pricing does not significantly differ by income level.
o In thesis two, I developed the previous methodology and disseminated a survey
in four countries to investigate the studied factors’ influence on RP
acceptability and AVs and SAVs adoption. I bridged a research gap by
simultaneously analyzing the impact of various factors on RP acceptability and
self-driving vehicle adoption and highlighted their interdependence and the
relevance of studying them at the same time. I found that people who are willing
to share their trips with others due to the application of RP opted for SAV, while
those who enjoy driving were less likely to choose AV. People with safety and
security concerns about AVs and SAVs were reluctant to use them. People who
care about the environment in their trip planning showed more acceptance of
RP.
o To improve and enhance the public acceptability of the RP scheme, authorities
should increase awareness about RP and clearly explain the goal of
implementing such a scheme and its positive effect on daily lives. Awareness
of new technologies and RP is an important factor in their adoption and
implementation. Therefore, educational campaigns through different platforms
100
and various methods should be held to inform people about the expected
benefits of driverless vehicles and RP, which will help raise their acceptability.
The public wants their governments to use RP revenues in areas where the
residents can feel their impact, such as enhancing PuT systems. Such policies
are critical as the public trust was found to be very low in government entities
regarding the use of revenues. Therefore, it is advised to clearly explain the
methods of utilizing the revenues from RP to satisfy the public’s requirements.
o Consequently, these research results can be further developed and used by other
researchers in future studies. For example, researchers may include other
variables (e.g., self-driving car legal liability and perceived comfort) and
conduct studies utilizing a prototype of AVs and SAVs alongside RP to learn
more about the impact of the presence of RP and the inclusion of AV and SAV
on influencing the potential user's acceptance to the concept of RP, AVs, and
SAVs. Additionally, future studies may employ a stated preference experiment
to examine the impact of RP features like toll value on the adoption of AVs and
SAVs, which may explain how various RP tolls may influence vehicle adoption.
• I utilized dynamic traffic assignment using the traffic macroscopic simulation software
"Visum" to investigate the impact of the emergence of AVs and SAVs on the Budapest
network, consumer surplus, and the impact of three RP strategies (static and dynamic)
on network performance and social welfare in alternative future scenarios. Three future
scenarios for the years 2030 and 2050 are presented and characterized by different
penetration rates of AVs and SAVs to reflect the uncertainty in the market share of these
future cars. Moreover, the travel demand of the developed scenarios was obtained from
BKK projections for the respective years, where the total predicted private transport
demand was 2.23 and 2.31 million trips per day for the years 2030 and 2050,
respectively. It is worth noting that the used Budapest “EFM Model” comprises over
30,000 links, around 15,000 nodes, and 1,200 zones.
o In thesis three, I utilized SBA using EFM Model within Visum to investigate
the impact of varying the share distribution of AVs and SAVs, in alternative
future traffic scenarios, on consumer surplus and network performance,
including average and maximum queue lengths, delays, volume, density,
utilization (scaled density), velocity, and vehicle kilometers traveled. The 2030
scenario combines CC, AVs, and SAVs, where CC is the dominant private
transport mode (Mix-Traffic Scenario), while the two scenarios for 2050
include AVs and SAVs only and are characterized by high adoption of AVs
(AV-Focused Scenario) or wide usage of SAVs (SAV-Focused Scenario). I
found that road users and authorities will benefit from the emergence of AVs
and SAVs; however, a higher replacement rate of CCs by SAVs will have a
more positive impact on traffic status, while a higher replacement rate of CCs
by AVs will maximize road user’s benefits.
101
o In thesis four, I analyzed the implications of applying different RP strategies
(static and dynamic) using the previously developed scenarios (i.e., Mix-Traffic
Scenario, AV-Focused Scenario, and SAV-Focused Scenario). The pricing
strategies consisted of a static-fixed toll (bridge toll scheme), a static-variable
toll (distance-based scheme), and a dynamic RP (link-based scheme), which all
were applied to the proposed future traffic scenarios. I highlighted that the
impact of RP schemes differs with different penetration rates of AVs and SAVs.
Nevertheless, considering the social benefits gained, implementing a dynamic
pricing strategy (Link-based Scheme) in the case of AV-Focused and SAV-
Focused scenarios shows better outcomes compared to other pricing schemes.
Conversely, the static pricing strategies (i.e., Bridge Toll and Distance-based
Schemes) outperform dynamic pricing strategies in the Mix-Traffic scenario.
o To improve the traffic performance, a higher replacement rate of CCs by SAVs
is recommended, while maximizing the user’s benefits can be achieved by a
higher replacement rate of CCs by AVs. The use of dynamic RP strategies is
recommended when AVs and SAVs have a high share of the transport market.
In comparison, it is advised to use static RP strategies in the presence of CC as
a dominant private transport mode (i.e., the bridge toll and distance-based
schemes). The impact of the distance-based scheme on traffic differed inside
and outside the charging zone; therefore, it is essential, in the case cordon
pricing is adopted, to carefully investigate the expected impacts of the proposed
strategies on the border and surrounding area of the charging zone. In all the
scenarios, the link-based scheme generated the maximum revenues (i.e.,
gathered tolls) along with being the best pricing strategy in terms of the change
of social welfare for AV-Focused and SAV-Focused scenarios and in terms of
producing a positive change in the case of the Mix-Traffic scenario. Therefore,
if the authority's objective of applying the RP strategy is to generate revenues,
then a link-based scheme can be an optimal solution.
o Consequently, these research results can be further extended by including the
effect of AVs, SAVs, and RP strategies on mode choice and people's mobility
behavior. It would be interesting to distribute a questionnaire (e.g., stated
preference experiment) that investigates the willingness to pay for adopting
AVs and SAVs, as well as the acceptable toll value for the public, along with
other parameters which can be inserted into the modeling process. Further
studies can be carried out to apply pricing schemes that reflect the First-best
Pricing Principle by matching the toll value dynamically with the marginal cost.
For instance, the users are charged a toll equivalent to the extra travel time they
cause for other travelers.
102
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List of Publications (Own):
[S1] M. Shatanawi, A. Alatawneh, and F. Mészáros, “Implications of Static and Dynamic
Road Pricing Strategies in the Era of Autonomous and Shared Autonomous Vehicles Using
Simulation-Based Dynamic Traffic Assignment: The Case Of Budapest,”2022, Research in
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[S2] M. Shatanawi, F. Abdelkhalek, and F. Mészáros, “Urban Congestion Charging
Acceptability: An International Comparative Study,” Sustainability, vol. 12, no. 12, p. 15, 2020,
https://doi.org/10.3390/su12125044. [IF:3.889].
[S3] M. Shatanawi, M. Hajouj, B. Edries, and F. Mészáros, “The Interrelationship between
Road Pricing Acceptability and Self-Driving Vehicle Adoption: Insights from Four Countries,”
Sustainability 2022, 14, 12798. https://doi.org/10.3390/su141912798. [IF:3.889]
[S4] I. Matalqah, M. Shatanawi, A. Alatawneh, and F. Mészáros, “Impact of Different
Penetration Rates of Shared Autonomous Vehicles on Traffic: Case Study of Budapest,”
Transportation Research Record, p. 03611981221095526, Jun. 2022,
https://doi.org/10.1177/03611981221095526. [IF:2.019]
[S5] M. Shatanawi, and F. Mészáros, “Implications of the Emergence of Autonomous
Vehicles and Shared Autonomous Vehicles: A Budapest Perspective,” Sustainability 2022, 14,
10952. https://doi.org/10.3390/su141710952 [IF:3.889].
[S6] M. Shatanawi, M. Ghadi, and F. Mészáros, “Road Pricing Adaptation to Era of
Autonomous and Shared Autonomous Vehicles: Perspective of Brazil, Jordan, and Azerbaijan,”
Transportation Research Procedia, vol. 55, pp. 291–298, 2021,
https://doi.org/10.1016/j.trpro.2021.06.033.
[S7] M. Shatanawi, S. Boudhrioua, and F. Mészáros, “Comparing Road User Charging
Acceptability in the City of Tunis and Damascus,” MATEC Web Conf., vol. 296, p. 02002,
2019, https://doi.org/10.1051/matecconf/201929602002.
[S8] M. Shatanawi, M. S. Csete, and F. Mészáros, “Road User Charging: Adaptation to the
City of Amman” University of Dunaújváros, Hungary, Nov. 2018, p. 10.
[S9] F. Meszaros, M. Shatanawi, and G. A. Ogunkunbi, “Challenges of the Electric Vehicle
Markets in Emerging Economies,” Period. Polytech. Transp. Eng., Feb. 2020,
https://doi.org/10.3311/PPtr.14037.
[S10] S. Boudhrioua and M. Shatanawi, “Implementation of Absolute Priority in a Predictive
Traffic Actuation Schemes,” Period. Polytech. Transp. Eng., Nov. 2019,
https://doi.org/10.3311/PPtr.14191.
[S11] A. Alatawneh, M. Shatanawi, and F. Mészáros, “Analysis of the Emergence of
Autonomous Vehicles Using Simulation-based Dynamic Traffic Assignment – The Case of
Budapest,” Periodica Polytechnica Transportation Engineering. [Under Review]
[S12] M. Shatanawi, U. Battsolmon, and Ferenc Mészáros, "Comparing Road User Charging
Acceptability in the City of Budapest and Ulaanbaatar," May 2019, vol. II, p. 16.
115
Appendix
Appendix Table 1 shows the percent distribution of the background characteristics “city
profile” of each city in Thesis I (i.e., Chapter two).
Appendix Table 1. Background characteristics/measurements "Cities profile" (%)
Background
characteristics/ City
Amman
Damascus
Tunis
Budapest
Ulaanbaatar
Total
Gender
Male
62.3
55.4
55.2
58.2
54.7
57.2
Female
37.7
44.6
44.8
41.8
45.3
42.8
Age
20–30
54.3
34.0
51.3
64.3
48.2
50.4
31–40
27.9
31.6
22.9
21.7
39.4
28.7
41+
17.8
34.4
25.8
14.1
12.4
20.8
Employment Status
Working
59.1
73.0
63.3
52.0
77.9
65.1
Student
31.6
16.4
20.4
44.8
16.9
26.1
Other
9.3
10.7
16.3
3.2
5.2
8.9
Income
Lowest
26.7
17.3
11.7
34.6
10.9
20.3
Low
39.5
44.3
13.9
41.3
6.9
29.2
Middle
25.9
19.8
39.9
18.3
35.5
27.8
Highest
7.8
18.6
34.5
5.8
46.8
22.7
Mobility
Car
64.6
45.0
50.7
20.6
59.7
48.0
Public Transportation
32.9
39.3
31.5
64.5
28.2
39.5
Foot/Bike
2.4
15.7
17.8
14.9
12.1
12.5
Owning a car
67.3
57.0
55.8
49.6
67.7
59.5
Driving license
88.6
78.6
87.8
78.3
89.5
84.5
Scheme Awareness
Awareness
38.3
63.2
47.2
50.6
53.2
50.4
Scheme Acceptance
Acceptability average
(on a 1-4 scale)
2.1
2.9
2.8
2.5
2.8
2.6
n5
247
244
240
249
249
1229
5
Total number of responses.
116
Appendix Table 2 shows the distribution of the socio-demographic characteristics in the
form of “country profiles” for the four countries in Thesis II (i.e., Chapter three).
Appendix Table 2. Socio-demographic characteristics “Country profiles”
n = 657
Brazil
Jordan
Hungary
Ukraine
Percentage%
38.8%
37.7%
11.7%
11.7%
Characteristics
Age
<20
5.1%
3.2%
9.1%
41.6%
20-26
30.6%
29.8%
39%
42.9%
27-32
37.3%
23.8%
19.5%
7.8%
33-38
4.7%
14.9%
11.7%
2.6%
39-44
5.5%
9.3%
6.5%
2.6%
45-50
4.7%
11.3%
5.2%
0%
>50
12.2%
7.7%
9.1%
2.6%
Gender
Male
43.1%
54.8%
57.1%
50.6%
Female
56.9%
45.2%
42.9%
49.4%
Educational Level
Elementary school certificate
2%
0%
0%
3.9%
High school certificate
20.4%
5.2%
27.3%
18.2%
Bachelor or Diploma
54.5%
60.9%
35.1%
46.8%
Postgraduate studies (Ph.D. or Masters)
19.2%
32.3%
36.4%
18.2%
Others
3.9%
1.6%
1.3%
13%
Employment Status
Full-time worker
43.5%
45.2%
48.1%
54.5%
Part-time worker
9.8%
7.7%
6.5%
45.5%
Unemployed
5.1%
5.2%
3.9%
0%
Student
30.6%
23.8%
27.3%
0%
Unpaid volunteer work
0.4%
1.2%
1.3%
0%
Retired
3.5%
4.4%
3.9%
0%
House Keeping
2%
9.7%
2.6%
0%
Others
5.1%
2.8%
6.5%
0%
Driving License
Yes
82.7%
81.5%
79.2%
54.5%
No
17.3%
18.5%
20.8%
45.5%
Car Ownership
Yes
55.7%
59.3%
41.6%
48.1%
No
44.3%
40.7%
58.4%
51.9%
117
Appendix Table 3 shows the items were used to extract the investigated factors in
Chapter three. The respondents were asked to assess their level of agreement with the items
based on a 5-point Likert scale from strongly disagree (1) to strongly agree (5).
Appendix Table 3. Items used to extract factors from the original survey’s questions
Awareness
AV_Awareness
Item 1
I am aware of the concept of autonomous cars.
Item 2
I am familiar with the topic of autonomous cars.
Item 3
I am confident that I am able to explain what an autonomous car is to anyone.
RP_Awareness
Item 4
I am aware of the concept of road pricing.
Item 5
I am familiar with the topic of road pricing.
Travel Behavior and Attitudes
PuT_Users
Item 6
I use public transport on a regular basis.
Item 7
I commute using public transport.
Item 8
I rely on public transport for the majority of my trips.
Enjoy_Driving
Item 9
I enjoy driving.
Item 10
Driving is exciting to me.
Item 11
I like the feeling of being in full control of my car.
Cycling_Users
Item 12
I cycle on a regular basis.
Item 13
I commute by cycling.
Item 14
I rely on cycling for the majority of my trips.
Walkers
Item 15
I walk on a regular basis.
Item 16
I commute by walking.
Item 17
I rely on walking for the majority of my trips.
Technology_Interest
Item 18
I think autonomous cars will be fun.
Item 19
I desire to learn about autonomous cars.
Item 20
I am excited to experience autonomous cars.
Cost_Oriented_Users
Item 21
The price of my trip will significantly influence my transport mode.
Item 22
My main priority is to travel at the lowest possible price.
Environmental_Oriented_Users
Item 23
The emission of my trip will significantly influence my transport mode.
Item 24
My main priority is to travel using less polluting vehicles.
Item 25
I take into consideration the environmental impact of my trip.
Sensing Traffic Problems
118
Sensing_Traffic_Problems
Item 26
I notice traffic congestion on a regular basis.
Item 27
I think road traffic is the primary source of air pollution.
Item 28
I think traffic causes a lot of noise, annoyance and disturbance.
Item 29
I think car parking is a significant problem.
Item 30
I think the public transport system is inadequate.
Item 31
I observe many traffic accidents and incidents on a daily basis.
Perceived Effectiveness
Perceived_Usefulness_RP
Item 32
I think the application of road pricing is likely to reduce travel time.
Item 33
I think the application of road pricing is likely to decrease the congestion level.
Item 34
I think the application of road pricing is likely to reduce air pollution.
Item 35
I think the application of road pricing is likely to reduce noise, annoyance, and
disturbance.
Item 36
I think the application of road pricing is likely to result in a better fuel economy.
Item 37
I think the application of road pricing is likely to reduce the number of
accidents and incidents.
Negative_Expectations_RP
Item 38
I think the application of road pricing is likely to increase the price of the trip.
Item 39
I think the application of road pricing is likely to make public transport modes
more crowded.
Item 40
I think the application of road pricing is likely to result in increasing social
inequality among the citizens.
Personal Effectiveness
Willingness_to_Share
Item 41
If road pricing is applied, I think that I will use public transport more in the
future.
Item 42
If the road pricing is applied, I think that I will reduce the number of
unnecessary trips that I make on a daily basis.
Item 43
If the road pricing is applied, I think that I will start using shared autonomous
vehicles more in the future.
Item 44
If the road pricing is applied, I think that I will share my cars with others in the
future.
RP_Perceived_Anxiety
Item 45
If road pricing is applied, I will protest against it
Item 46
If the road pricing is applied, I will change my traveling routes to avoid paying
the tolls.
Item 47
If the road pricing is applied, I am afraid that I would not understand how road
pricing works.
Safety and Security
AV_Safety_Security_Concerns
Item 48
I will be worried if any equipment or system fails in autonomous cars during
any adverse conditions (e.g., heavy rainfall, fog)
Item 49
I am afraid about the legal liability for owner(s)/ operator(s) of autonomous
cars.
Item 50
I am concerned about the possibility of autonomous cars’ computer systems
being hacked.
119
Item 51
I am concerned about data privacy when using autonomous cars (e.g.,
disclosure of my travel destinations to third parties).
Item 52
I am concerned how autonomous cars will interact with other road users (e.g.,
conventional vehicles and bicycles).
Item 53
I think autonomous cars will not be safe to use.
Item 54
I will not feel secure to use autonomous cars on a daily basis.
AV_Perceived_Ease_of_Use
Item 55
I think it will be easy to learn how to use autonomous cars.
Item 56
I think autonomous cars will be simple to control.
Item 57
I think autonomous cars will be easy to use.
Social norms concerning RP acceptability
Social_Norm
Item 58
People whose opinions are important to me think that I should accept the
application of road pricing.
Item 59
My friends, family, and colleagues expect me to accept the application of road
pricing.
Equity
Equity
Item 60
I think the application of road pricing will be in my favor.
Item 61
I think the application of road pricing will benefit me more than other road
users.
Fairness
Fairness
Item 62
I think road pricing should be implemented for all vehicles without exemptions
Item 63
I think road pricing should vary according to the congestion level.
Item 64
I think road pricing should vary according to the quality of the road
