Impacts of Autonomous Vehicles on Traffic Flow Characteristics under Mixed Traffic Environment: Future Perspectives

Abstract

Vehicle automation and communication technologies are considered promising approaches to improve operational driving behavior. The expected gradual implementation of autonomous vehicles (AVs) shortly will cause unique impacts on the traffic flow characteristics. This paper focuses on reviewing the expected impacts under a mixed traffic environment of AVs and regular vehicles (RVs) considering different AV characteristics. The paper includes a policy implication discussion for possible actual future practice and research interests. The AV implementation has positive impacts on the traffic flow, such as improved traffic capacity and stability. However, the impact depends on the factors including penetration rate of the AVs, characteristics, and operational settings of the AVs, traffic volume level, and human driving behavior. The critical penetration rate, which has a high potential to improve traffic characteristics, was higher than 40%. AV’s intelligent control of operational driving is a function of its operational settings, mainly car-following modeling. Different adjustments of these settings may improve some traffic flow parameters and may deteriorate others. The position and distribution of AVs and the type of their leading or following vehicles may play a role in maximizing their impacts.

Full text

sustainability
Review
Impacts of Autonomous Vehicles on Traffic Flow Characteristics
under Mixed Traffic Environment: Future Perspectives
Mohammed Al-Turki 1 ,* , Nedal T. Ratrout 1, Syed Masiur Rahman 2and Imran Reza 3


Citation: Al-Turki, M.; Ratrout, N.T.;
Rahman, S.M.; Reza, I. Impacts of
Autonomous Vehicles on Traffic Flow
Characteristics under Mixed Traffic
Environment: Future Perspectives.
Sustainability 2021,13, 11052.
https://doi.org/10.3390/
su131911052
Academic Editors: Efthimios Bothos,
Panagiotis Georgakis,
Babis Magoutas and Michiel de Bok
Received: 24 August 2021
Accepted: 2 October 2021
Published: 6 October 2021
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Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
1Department of Civil & Environmental Engineering, King Fahd University of Petroleum & Minerals,
Dhahran 31261, Saudi Arabia; nratrout@kfupm.edu.sa
2Research Institute, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia;
smrahman@kfupm.edu.sa
3Department of Civil & Architectural Engineering, University of Wyoming, Laramie, WY 82071, USA;
ireza@uwyo.edu
*Correspondence: moe_kt@hotmail.com; Tel.: +966-56-824375
Abstract:
Vehicle automation and communication technologies are considered promising approaches
to improve operational driving behavior. The expected gradual implementation of autonomous
vehicles (AVs) shortly will cause unique impacts on the traffic flow characteristics. This paper focuses
on reviewing the expected impacts under a mixed traffic environment of AVs and regular vehicles
(RVs) considering different AV characteristics. The paper includes a policy implication discussion for
possible actual future practice and research interests. The AV implementation has positive impacts
on the traffic flow, such as improved traffic capacity and stability. However, the impact depends
on the factors including penetration rate of the AVs, characteristics, and operational settings of the
AVs, traffic volume level, and human driving behavior. The critical penetration rate, which has a
high potential to improve traffic characteristics, was higher than 40%. AV’s intelligent control of
operational driving is a function of its operational settings, mainly car-following modeling. Different
adjustments of these settings may improve some traffic flow parameters and may deteriorate others.
The position and distribution of AVs and the type of their leading or following vehicles may play a
role in maximizing their impacts.
Keywords:
autonomous vehicle (AV); road capacity; traffic stability; mixed traffic environment;
regular vehicle (RV); policy implication
1. Introduction
The term autonomous vehicle (AV) is usually referred to as an autonomous vehicle
that is considered a fully automated vehicle with level 5 automation without connectivity
capabilities [
1
]. It has a self-driving system that can perform all the driving and operational
tasks without human conduction. AVs can be divided into connected and autonomous
vehicles [
1
–
3
]. Connected vehicle (CV) technology and automatic driving are two different
technologies [
4
]. The term CV refers to any type of vehicle, including the RVs; with
connectivity capabilities that access the information of their surrounding vehicles or the
traffic infrastructure.
The term AV may refer to different types of AVs in terms of vehicle automation.
Different levels range from partial automation to full automation. Partial automation may
help to improve human driving behavior by utilizing advanced assisting driving systems
(ADAS). The main rule of such systems is to improve driving comfort, reduce human
driving errors, minimize the necessity for physical road signals and vehicle insurance,
and improve safety [
4
–
6
]. Most of these are based on adaptive cruise control (ACC)
systems, which are available now in a wide range of the existing models of the RVs [
5
,
7
].
By automatically controlling the throttle or the brake, the ACC system can control the
car-following behavior, such as adjusting its speed and providing a specified distance,
Sustainability 2021,13, 11052. https://doi.org/10.3390/su131911052 https://www.mdpi.com/journal/sustainability

Sustainability 2021,13, 11052 2 of 22
location, and acceleration from the trajectory data by using on-board sensors [
2
,
5
,
8
–
10
].
These systems are considered partial (semi) automated vehicles that only offer autonomous
longitudinal following control [
1
,
11
]. The cooperative adaptive cruise control (CACC) is
an updated and improved driver assistance system built upon the ACC system. It uses
more information collected from the advanced V2V communication technologies [
7
] to
automatically adjust the longitudinal control depending on the behavior of its leading
and following vehicles [
7
,
8
,
12
]. With both ACC and CACC systems, the drivers are
still responsible for actively steering the vehicle, keeping lanes, and monitoring traffic
conditions. However, unlike ACC systems, CACC has direct communication with the
vehicles ahead, using dedicated short-range communication (DSRC), which can enlarge
the detection range of the distance sensors [
4
], it can provide a range of 3000 ft in diameter
with the possibility of an extended range using multiple transmitters [
13
]. As a result,
compared to ACC, CACC can gather more information which enables more accurate and
effective performance in a faster manner [2,9,14].
AV implementation may lead to several impacts on urban mobility and city design [
15
].
AVs may lead to an increase in urban sprawl, resulting in increased travel time, energy con-
sumption, and air pollutants [
15
]. Additionally, AVs will have impacts on individual travel
behavior such as trip generation, mode choice, vehicle ownership, and travel time [
16
–
18
].
Hamadneh and Esztergar-Kiss (2019) compared the behavior of travelers before and after AV
implementation assuming travelers are willing to wait and share their trips with others [
17
].
Their results indicated that a single shared AV could replace eight RVs, reducing travel time
and travel distance by 17% and 20%, respectively. In their recent study, Hamadneh and
Esztergar-Kiss (2021) aimed to consider the mixed traffic conditions of AVs with conventional
transport modes, including RVs [
18
]. Their results indicated that the travel time decreases as
AV implementation increases.
In the case of a traffic environment which includes only RVs, the stochastic nature of
human driving behavior is critical to determining traffic flow efficiency and road opera-
tional capacity [
13
] and traffic stability [
19
]. The reaction times, desired speed, selected
headways, and safe distance in addition to their heterogeneous reactions to traffic dis-
turbances, such as performing lane changes or applying sudden brakes, are examples
of such behaviors [
13
,
20
]. Although the main objective of AV implementation in traffic
networks is to improve the comfort and safety of users, and environmental benefits during
their mobility, AV’s role could be pivotal in improving traffic flow characteristics [
21
].
Both RVs and AVs take the same amount of road space; however, the implementation
of AVs will lead to different impacts on the traffic system. The exchange of information
through the vehicle to vehicle (V2V) and vehicle to infrastructure (V–I) communications
may help the AV to adjust their driving behaviors based on real-time traffic conditions.
In addition, the AV’s expected impacts are mainly resulted from their unique operational
characteristics, including car following, lane changing, speed, reaction times, and safety
distance parameters. Compared to RVs, AVs will have a faster response to the different
traffic conditions with better anticipation of the action of preceding vehicles [
22
–
24
]. Some
studies showed that AVs have negligible reaction time delays as compared to RVs [
25
].
Furthermore, different reports showed that the AV implementation in the traffic flow
might harmonize and increase the mean speed in the traffic flow [
19
,
25
] and decrease the
speed variability [
23
]. When the desired speed is set, the AV will barely deviate from
this speed [
22
]. In addition, the acceleration and deceleration of AVs are generally much
smoother than RVs [
22
]. Additionally, the AV will lead to shorter headways and safety dis-
tances in the flow [24,25]. Compared to RVs, AVs have smarter lane-changing maneuvers
in lane changing [
25
]. Therefore, the implementation of AVs will cause fewer disturbances
and less heterogeneity in the traffic leading to maximizing throughput in the traffic [
8
,
24
].

Sustainability 2021,13, 11052 3 of 22
The adjusted characteristics impact the traffic flow elements such as road capacity
parameters and the components of the traffic stability. The road capacity is mainly affected
by different factors such as traffic composition, road conditions, and driving behaviors
of the vehicles. Traffic composition, including the vehicle type with their specific driv-
ing behavior and the penetration of each type, is a significant factor that may affect the
capacity [
4
]. Capacity is highly dependent on the longitudinal time headways and the
lateral movements of vehicles [
22
]. Traffic flow stability is also an important parameter that
determines the efficiency of the traffic networks. Traffic stability indicates maintaining the
traffic capacity without a traffic breakdown incident. When traffic volume exceeds capacity,
automobiles are compelled to perform cyclic acceleration–deceleration operations, result-
ing in unstable traffic flows influenced by the creation and spread of shockwaves [
13
,
20
].
One of the important factors in this regard is the speed and time gap distributions. With a
smaller standard deviation, the flow will be more stable, and there will be fewer unexpected
breakdowns. Traffic hysteresis and oscillations develop in the flow due to automobile
lane-changing and car-following behavior [
13
]. Examples may include lane changes near
merges and diverge section, lane drops, or changes of road geometrics.
Due to its improved operational characteristics, the implementation of the AV is
expected to overcome the problems of stochastic human driving behavior and positively
impact traffic flow efficiency. Several studies have investigated the possible impacts of
AV implementation for either complete AV or mixed traffic environments with RVs. Due
to the lack of available observed traffic data, most of these studies focus on modeling
and simulation. Based on the literature review, it appears that there are limited numbers
of papers that reviewed the impacts of AV implementation on different related aspects.
Hoogendoorn et al. (2014) reviewed the general impacts of AVs on traffic flow efficiency [
23
].
Narayanan et al. (2020) reviewed the contributing factors affecting the impacts of AVs [1].
Most of the published papers focused on reviewing the impacts on traffic systems assuming
a complete AV environment. Additionally, the literature indicated that a large proportion
of papers focused on the impacts of the partial automation level of AVs. However, it is
expected that the implementation of different types of AVs, including complete AV level in
the traffic environment, will occur gradually, and therefore, it will cause different impacts
on the traffic system. None of the published reviews adequately focused on reviewing the
impacts on the traffic flow characteristics under a mixed traffic environment with RVs.
This review study aims to fill the critical research gap in the future development
of AVs in urban networks. The main objective of the current study was to include and
discuss an extended review of the above-mentioned topic under partial AV implementation.
The concepts of road capacity and traffic stability were considered to analyze the impacts of
the AVs on the traffic flow characteristics. Unlike other reviews in the literature, this review
discussed the impacts under different important aspects of the topic, such as different
types of AVs representing different automation levels and connectivity capabilities and
network types to enhance the analysis capacity for some meaningful findings. Based on the
findings of the current review, a policy implication was included to significantly help the
traffic community to maximize the benefits of AV implementation by developing effective
policies for future traffic operation and control under a mixed traffic environment.
The remaining sections of this paper are organized as follows. Section 2includes the
research methodology of the paper. Section 3includes a brief discussion of the impacts of
the complete AV implementation. In Section 4, a comprehensive literature review of the
different related studies is included considering different types of AVs under partial AV
implementation. In Section 5, the primary factors of the impacts of AV implementation on
mixed traffic environments are discussed in detail. Section 6includes a policy implications
discussion that intended to provide a holistic lens to the interpretation of the main findings
of this research for the actual future practice and further research interests. Finally, Section 7
includes the conclusions and recommendations of this work.

Sustainability 2021,13, 11052 4 of 22
2. Research Methodology
Despite covering an enormous body of literature, which is still growing, this study
includes a systematic review that aimed at finding the impacts of different types of AVs
(e.g., ACC, CACC, AV, CAV) on the traffic capacity and stability under mixed traffic
environment with the RVs (Figure 1). The planning part of this systematic review entails
creating the study purpose and question, keywords, and a set of inclusion and exclusion
criteria. Thus, “autonomous vehicle”, “road capacity”, “traffic stability”, and “mixed
traffic environment” were selected to be the search keywords. The inclusion criteria were
specified to peer-reviewed journal papers published in English and related to the research
objective. However, this review has ignored the impacts of AVs on traffic safety, travel
planning, including travel time and road choice, driver comfort, environmental issues,
and economic and social impacts. The impacts related to the application of the platooning
concept or the use of dedicated AV lanes in the traffic network were not considered.
The selected publications are mainly related to investigating the impacts of AVs on the
traffic characteristics in mixed traffic environments sharing the same traffic network lanes
with other different types of vehicles, including RVs. The search’s first publishing date was
not specified; however, April 2021 was chosen as the end date. The following limitations
should be noted in this review approach: (a) exclusion of non-peer-reviewed full-text
papers that are freely available online, including conference proceedings, book chapters,
and white papers, and (b) unintentional bias of the authors may influence how the review
and findings are carried out and interpreted. The second phase entails the reviewing
of pertinent articles. Throughout the extraction procedure, the complete contents of the
publications were searched and read. At the beginning of this stage, according to differing
levels of complexity, the AV types are divided into three main categories. For each category,
critical ideas about representative models are reviewed and explained. Furthermore, their
impacts on road capacity and traffic stability are critically reviewed along with their critical
related factors. In general, the distinctions and links that connect traffic flow and AV driving
behavior under a mixed traffic environment are extensively discussed in this research.

Sustainability 2021,13, 11052 5 of 22
Figure 1. Methodology flowchart.
3. Impacts of the Complete AV Implementation
Many related studies have been conducted to evaluate the impacts of the complete
implementation of different types and models of AVs in traffic networks. In general, most of
these studies have confirmed that the complete implementation has positive impacts on the
traffic flow characteristics, mainly in terms of improved traffic capacity. For example, Van
Arem et al. (2006) concluded that complete CACC implementation increases the traffic flow
and capacity in the highway system [
26
]. Tientrakool et al. (2011) concluded that compared
to a complete RV environment, a complete CACC environment in the urban network
increases the capacity by 273% [
27
]. Shladover et al. (2012) concluded that the capacity
of CACC traffic is doubled compared to a RV environment [
28
]. Bohm and Häger, (2015)
showed that when the traffic volume is high, the complete AV (Level 3) traffic increases the
capacity parameters compared to the complete environment of RVs [
29
]. The delay and
number of stops decreased by 56% and 54%, respectively, while the speed increased by 34%.
However, in the case of low volume, the delay increased by 1.3%, speed decreased by 0.38%,

Sustainability 2021,13, 11052 6 of 22
and the number of stops increased by 2.9%. Maurer et al. (2016) found that compared to
complete RVs, the capacity of complete AV environment increased by 40% and 80% in
urban network and highway respectively, using the improved symmetric two-lane cellular
automaton (STCA) model. They concluded that the capacity with complete AVs increase
from 2000 veh/h at 0% penetration rate of AVs to 3070 veh/h [
30
]. Mena-Oreja et al. (2018)
also confirmed that, based on the proper selection of the maximum platoon length and the
desired gap, the traffic flow of the complete CACC will increase by almost 39% compared
to complete RV traffic [
31
]. The findings from Abdulsattar et al. (2020) also supported
that a complete CAV environment will improve capacity by 250% compared to RV traffic
in multi-lane highway systems [
13
]. In contrast to the above findings, Lu et al. (2020)
contradicted the significantly improved capacity of the complete CAV implementation [
24
].
4. Impacts of Partial AV Implementation
Different studies in the literature have investigated the possible impacts of partial
implementation of AVs on traffic flow characteristics under a mixed traffic environment.
This literature included all of the related published studies during the period from 2003
up to 2020. Tables 1–3summarize the main findings of these studies, respectfully, for the
three categories: ACC environment, CACC and CAV environment, and AV environment,
respectively. The variables of each study, including AV type, network type, and the study
type, are included. The following parts of this section will discuss these findings concerning
the two main traffic characteristics: traffic capacity and stability for each AV type. The next
section of the paper will include an extended discussion about the primary factors of the
impacts of AV implementation on a mixed traffic environment.
Table 1. Review of the studies related to the impacts of ACC on the traffic flow characteristics.
Study
Study Variables
Main Findings
AV Type Network
Type Study Type
[32] ACC Multi-lane
highway Simulation
•Flow increased by 3% per 10% increase of the penetration rate of ACC. Outflow increased
by 2.4% per 10% increase of the rate of ACC
•Low rates of ACC (25%) can decrease congestions
[33] ACC Closed ring
road
Meso-
simulations
•Low rates of ACC increased the capacity
•Capacity and flow increased as ACC rate increased
•Increased rate of RVs caused high susceptibility of congestion
[34] ACC
Single-lane
and ring
roads
Simulation
•With time gaps less than 1.1 s, capacity rose linearly with increasing ACC rate; however,
with time gaps greater than 1.5 s, capacity declined as rates of ACC led to more improved
stability
[8] ACC Highway Macroscopic
analysis
•ACC improved the stability and increased the flow
[22] ACC Multi-lane
road
Simulation
(considered
lane
changing)
•ACC had negative impacts on the flow and capacity
•The positive impacts were found at rates above 70%
•The capacity drop was slightly higher with the ACC implementation and was increasing
as the rate increasing
•Decreased lane changes did not show significant impacts
[35] ACC Multi-lane
highway
Micro-
simulation
•ACC performance was highly affected by the parameter settings. Inappropriate settings
increased the collision risks and caused traffic disturbances
•Optimal settings are expected to be different for different traffic conditions
•Higher time gaps and deceleration rates improved stability and safety; but it decreased
capacity
[36] ACC Highway Review
•Classical ACC increased the speed disturbance due to long time headway, however, the
dynamic rules of modified ACC types decreased it and therefore that improved the flow
•The likelihood of a traffic breakdown is determined by the ACC’s dynamic parameters

Sustainability 2021,13, 11052 7 of 22
Table 2. Review of the studies related to the impacts CACC and CAV on the traffic flow characteristics.
Study
Study Variables
Main Findings
AV Type Network
Type
Study
Type
[37] CACC Multi-lane
highway Simulation
•Partial CACC implementation increased the flow from 2100 (with 100% RVs traffic) to 2900 (with
20% RVs, 20% ACC, 60% CACC traffic)
•A critical time gap of 1.4 sec was found to be the limit between the deterioration and improvement
[26] CACC Highway Simulation
•At low traffic volumes, no significant impacts of different rates of CACC
•At high traffic volumes, increased CACC rates improved stability, increased throughput, and
speed, and decreased shockwave impacts
•With rates less than 40%, the impacts were minimal
[38] CACC Single-lane
highway Simulation
•Increased headway variability due to the mixed traffic had minor impacts on the stability
•CACC quickly damped shockwaves at low rates (50%), and that improved more with higher rates
[27] CACC
Urban
signalized
intersec-
tion
Capacity
analysis
•Compared to complete RV, complete ACC increased the capacity by 43%, while complete CACC
increased it by 273%
•Capacity increased slightly until the CACC rate exceeded 85%
[28]CACC,
ACC
Single-lane
highway
Micro-
simulation
•ACC did not show improvement in the capacity; however, CACC significantly increased the
capacity mainly with higher rates
•Increased rates of CACC had significant impacts on traffic flow, but the rate of ACC had minor
impacts
[14] CACC Review
•The positive impacts of CACC on the stability and throughput were only found with CACC rates
of more than 40%
[9]CACC,
ACC
Three-lane
highway
Micro-
simulation
•Platoon size had small impacts on the capacity
•Capacity was increasing as the rate of CACC increasing
[39]CACC,
ACC
Urban
road
Field
testing
•CACC increased the capacity and improved the stability
[40] CACC Four-lane
highway Simulation
•CACC implementation increased the capacity and improved the stability
•At low traffic volume, no significant impacts of different penetrations rates
•At high traffic volume, increased CACC rates led to a significant increase in the flow
•Rates of less than 40% had small impacts
[41] CAV
Signalized
intersec-
tion
Micro-
simulation
•CAVs with a smoother acceleration/deceleration profile had negative impacts on capacity
[34]CACC,
ACC Highway Numerical
simulation
•Compared to ACC, CACC improved the stability
•CACC increased the capacity and improved traffic dynamics, mainly at on-ramp bottlenecks
[8] CAV, AV Single-lane
highway
Micro-
simulation
•CAV enabled a more reliable driving experience than AV
•CAV with shorter headway increased the capacity and improved stability even at low rates
•CAV with longer headway decreased the capacity and increased delays; however, it improved
stability
[11]CACC,
ACC, AV
Urban
signalized
intersec-
tion
Simulation
•Different behavioral models and parameters of AVs had different impacts on traffic flow
•AV penetration rate had similar impacts in urban networks and highways
•Capacity and flow were increasing, and the delay was decreasing as the AV rate was increasing
[4] CAV Multi-lane
highway
Simulation
(considered
lane
changing)
•CAV’s capability to maintain shorter headways was negligible at lower rates
•Capacity increased with an increase of CAV rate, and that was more significant with rates of higher
than 30%
•Higher time gaps improved the stability and safety; however, it decreased the capacity

Sustainability 2021,13, 11052 8 of 22
Table 2. Cont.
Study
Study Variables
Main Findings
AV Type Network
Type
Study
Type
[42] CAV Multi-lane
highway
Simulation
(considered
lane
changing)
•As the rate of CAV increased, traffic flow became smoother owing to a reduction in excessive
deceleration rates. When rates of CAV are between 40 and 50%, the proportion of stop-and-go
traffic was decreased
[31] CACC Multi-lane
road
Micro-
simulation
(considered
platoon-
ing)
•Compared to complete RV traffic, complete CACC traffic, with a maximum platoon length of 2
vehicles and desired gaps of 10, 5, 1 m, increased the flow by 9.39%, 26.09%, and 39.21%,
respectively,
•with rates of less than 20%, capacity and flow decreased; however, with higher rates, capacity and
flow increased
•Desired gaps and maximum platoon length were important factors of the capacity improvement
[43]AV, ACC,
CAV Ring road
Simulation
(considered
lane-
changing)
•The average speed decreased, while density and congestion increased with increased rates of AVs
•With high traffic volume scenarios, high rates of CAVs lead to increased capacity, smoother lane
changes, maintained high speed, and improved stability; however, low rates showed small
negative impacts
•With low traffic volume scenarios, compared to RV traffic, the CAV’s implementation showed
negative impacts even with higher rates
[20] CAV
Single-lane
circular
road
Simulation
•The portion of stop-and-go traffic decreased and led to improved stability with the increasing rate
of CAVs
•Rates from 10% to 50% showed similar performance
•CAV distribution did not show significant impacts
[24] CAV Urban
network
Micro-
simulation
•Capacity, stability, and flow were improving with the increase of CAV rate
•Flow significantly increased with 50% CAV rate
[44]ACC,
CACC
Multi-lane
highway
Numerical
simulation
(consid-
ered lane
changing)
•An increase in CACC rate significantly decreased traffic congestion and improved the capacity and
stability
•ACC implementation did not show any improvement in the stability
[1,44] CAV Highway Simulation
•CAV with longer time gaps improved stability and safety; however, it decreased the capacity
•The level of the impacts was increasing with the increase in the rate of CAV
[7]CV, AV,
CAV
Network
level
Meso-
simulation
•CVs and AVs, mainly with higher rates, led to significant improvement in traffic flow, capacity;
however, AVs were more significant than CVs
[13] CAV Multi-lane
highway
Simulation
(considered
lane
changing)
•CAV significantly increased the capacity with high rates (50% and above); however, lower rates
(less than 40%) did not show improvement
•The capacity in the free flow was directly proportional with the rate of CAV
[45] CAV Multi-lane
highway
Micros
simulation
(considered
lane
changing)
•The average lane change of RVs increased till 30% rate of CAVs was reached. After that, it
decreased as CAVs rate increased
[46] CAV
A
single-lane
ring road
Numerical
simula-
tions
•Stability was related to the driver’s reaction delay, rate of CAV, and the information obtained by
CAVs
•CAV implementation increased the traffic flow
•Driver’s reaction delay decreased the stability
•As the rate of the CAVs increased, stability improved, and the propagation of lead vehicle
disturbance gradually disappeared

Sustainability 2021,13, 11052 9 of 22
Table 3. Review of the studies related to the impacts of AVs on the traffic flow characteristics.
Study
Study Variables
Main Findings
AV Type Network
Type Study Type
[23] AV Review
•AV implementation had positive impacts on the capacity and stability
•Congestion delay decreased by 30%, 60% with 10%, 50% rates of AVs, respectively
•Impact of AVs was highly affected by the time headway and speed choice
[29]Full AV (level
3)
Urban
network Simulation
•With high traffic volume scenarios, complete AV traffic increased the capacity, decreased
delay and number of stops by 56% and 54%, and increased speed by 34%
•With low volumes scenarios, complete AV traffic increased the delay by 1.3%, decreased
speed by 0.38%, and increased the number of stops by 2.9%
[47] AV
Highway
and
signalized
intersection
Mathematical
modeling
•Capacity increased as the rate of AVs increased
•A single RV in the lane decreased the speeds and therefore decreased the capacity
improvement
[48] AV
Different
network
levels
Macro-
simulation
•At high volume scenarios, the capacity of traffic signals was doubled, however, at low
volumes, no improvement was found
[25] AV Multi-lane
highway
Meso-
simulation
(considered
lane-
changing)
•AVs significantly improved the capacity, stability, and free-flow speed, mainly with high
rates
•Car-following maneuvers contributed more to the improvement than the smarter
lane-changing
•Different lane-changing rules had minor impacts
•Penetration rate of 50% was the optimal rate of lowering congestion level
[49] AV Multi-lane
highway
Theoretical
framework
•Strict segregation of AVs and RVs decreased the capacity; however, mixed-use policies
increased it
[50] AV Urban
network Simulation
•Slow improvement of network performance was observed until 40% AV rate; however,
after 60% rate, the performance degraded
[51] AV Single-lane
circular road
Field
experiment
•AV improved the stability
•A single AV significantly decreased the stop-and-go waves that were produced by 20 RVs
around it
•A 5% rate of AVs led to significant positive impacts on stability
[52] AV Single-lane
ring road
Control-
theoretic
study
•AVs significantly suppressed the unstable waves and led to smoother flow
•Speed increased by more than 6%, with 5% rate of AV
[19] AV Single-lane
highway
Numerical
experiment
•AV improved the stability and decreased the uncertainty of the RV behavior
•Uncertainty of RVs was lower in congested conditions compared to the free flow
•The improvement was more significant with high rates of AVs (50%)
•Reaction time and position of AVs showed negligible impacts
4.1. Traffic Capacity
This part will include a discussion of the findings related to the impacts of AV im-
plementation on the traffic capacity and its components for each AV type. Based on the
literature, different research has shown that the implementation of different levels and
types of AVs in the network will significantly impact the improvement of the traffic capacity.
The following subsection includes detailed literature for each type.
4.1.1. ACC Environment
Some studies have shown that the implementation of ACC may have significant
impacts on traffic capacity. For example, using the Enhanced Intelligent Driver Model
(EIDM), Kesting et al. (2010) demonstrated that the ACC may increase traffic flow and
recovery from traffic breakdowns mainly at higher penetration rates [
32
]. The maximum
flow was increased by 0.3% per 1% increase in the penetration rate of ACC, and the outflow
from traffic jams was increased 0.24% per 1% increase in the penetration rate of ACC. Even

Sustainability 2021,13, 11052 10 of 22
low penetration rates of ACC (25%) can decrease traffic congestion. Jerath and Brennan
(2012), using General Motors’s car-following model, concluded that the ACC may increase
the traffic capacity even at lower penetration rates [
33
]. The capacity and traffic flow
increase mainly with increase in ACC penetration rates. Ntousakis et al. (2015) used
different time-gap settings and different network levels to conclude that smaller time gaps
increased the capacity linearly with the penetration rate [
34
]. In contrast to the above
positive impacts [
28
], using realistic headway choices, ACC did not significantly increase
the capacity of single-lane highways. Calvert et al. (2017) confirmed that ACCs with
smaller than 70% penetration rates have a negligible negative impact on traffic capacity [
22
].
The capacity drop is slightly increased due to the increase in the ACC rate. Li et al. (2017),
using modified IDM, indicated that ACC may reduce the capacity owing to higher time
gaps and deceleration rates [35].
4.1.2. CACC or CAV Environment
Some studies have confirmed that the implementation of CACC or CAV together would
have significant impacts on traffic capacity [
53
]. Different studies indicated the capacity im-
provement due to CACC implementation, predominantly with higher rates [
9
,
28
,
40
]. Mi-
lanés et al. (2013), based on a field test, confirmed that CACC would increase the capacity
of urban networks due to the reduced inter-vehicular gaps [
39
]. Another study particularly
focused on on-ramp bottlenecks and found that CACC increases the capacity and traffic dy-
namics [
34
]. CAV with shorter headways increases the flow and capacity mainly with an
increase in the penetration rate [
8
]. This improvement was significant even with low penetra-
tion rates. However, the positive impacts of the CAV were observed only at high traffic volume
scenarios with higher penetration rates [
43
]. The improved characteristics of the CAV, such
as the reduction of the time headways, gaps, and reaction times lead to improved capacity.
However, for the scenarios of low traffic volume, CAV did not perform well compared to RVs,
even with higher penetration rates due to the CAV’s inability to overcome the speed limits.
Lu et al. (2020) and Zhou et al. (2020) also confirmed that CAV implementation increases the
capacity [
24
,
44
]. Abdulsattar et al. (2020) confirmed these findings mainly with high penetration
rates due to the short following distance between two consecutive CAVs [
13
]. In contrast to
the above positive impacts, CAV implementation with long-desired time gaps decreases the
capacity [
1
,
4
,
44
]. When the penetration rate of CACC is less than 20%, the capacity and the flow
decreased as resulted by a high number of aborted maneuvers due to the obstruction caused
by RVs [
31
,
43
]. However, when the rate of CACC is more than 20%, the capacity and flow
increased which is also supported by previous studies.
4.1.3. AV Environment
Some studies have shown that the implementation of AVs may have significant
impacts on traffic capacity [
23
]. The capacity and flow increase as the penetration rate
of AVs increases [
47
]. Using different behavioral models of AVs, Bailey (2016) concluded
that, as the AV penetration rate increases, the flow of the AV increases and the delay
decreases [
11
]. Liu et al. (2017) also confirmed that the implementation of AVs significantly
increases highway capacity [
25
]. Fakhrmoosavi et al. (2020) concluded that CVs and AVs
increase the intersection capacity [7].
4.2. Traffic Stability
This subsection includes a discussion of the findings related to the impacts of AV imple-
mentation on traffic stability and its components for each AV type. The components include
throughput (flow), speed, and shockwave formation and propagation, and traffic congestion.
The following subsections include detailed literature for each type of traffic environment.
4.2.1. ACC Environment
Some studies have shown that the implementation of ACC has significant impacts
on traffic stability. Ntousakis et al. (2015) concluded that higher penetration of ACC

Sustainability 2021,13, 11052 11 of 22
may improve traffic stability by reducing the intensity of congestion waves [
34
]. Li et al.
(2017) showed that ACC with longer time gaps and higher deceleration rates improve the
stability of highways [
35
]. In contrast to the above positive impacts, Zhou et al. (2020)
concluded that the implementation of ACC in highway deteriorates the stability of traffic
flow stability [
43
]. Kerner (2020) in his study informed that the classical ACC model
causes speed disturbance due to the long headway and therefore deteriorate the stability
by initiating traffic breakdowns. However, the improved ACC models decrease these
disturbances and either do not have negative impacts or improve the stability [36].
4.2.2. CACC or CAV Environment
Implementation of CACC or CAV is expected to have significant impacts on traffic
stability. Van Arem et al. (2006), using good vehicle dynamics and driver behavior models,
concluded that higher penetration rates of CACC in high traffic volume scenarios signif-
icantly improve traffic stability, increase flow, reduce shockwave impacts, and increase
the average speed [
26
]. Schakel et al. (2010), using the IDM+ car-following model, con-
cluded that CACC improves traffic stability by the fast dampening of the shockwaves even
with low penetration rates [
38
]. The simulation results of several studies confirmed the
improved stability due to CACC implementation [
39
,
40
,
54
]. Narayanan et al. (2020) also
confirmed that CAV with the application of long-desired time gaps decrease speed disper-
sion and improve traffic stability and safety [
1
]. Additionally, Talebpour and Mahmassani
(2016) and Ye et al. (2018) supported the fact that CAV implementation improves stability
by the reduction of shockwave impacts [
8
,
42
]. Makridis et al. (2018) concluded that CAVs
had a positive impact on traffic stability which improves with the increase in penetration
rates due to smoother lane changes and the maintained high speed of the CAV in high
traffic volume scenarios [
43
]. Using the stochastic Lagrangian model, F. Zheng et al. (2019)
concluded that CAVs improve the stability of the traffic flow by reducing the stop-and-go
waves [
20
]. CAV implementation improves the stability and increases the flow mainly
with the increase of CAV penetration [
24
]. Zhou et al. (2020) concluded that the increase
of CACC penetration significantly decreases traffic congestion and improves traffic stabil-
ity [
44
]. CAV implementation increases traffic flow; however, the information from vehicles
ahead of CAV plays an important role [
46
]. In contrast to the above findings, Makridis
et al. (2018) concluded that low penetration rates of CAVs led to minor negative impacts
on traffic stability [43].
4.2.3. AV Environment
The review made by Hoogendoorn et al. (2014) indicated that AV implementation has
positive impacts on traffic stability. Congestion delay can be decreased by 30% and 60%
with 10% and 50% penetration rates of AVs, respectively, due to their increased throughput
in traffic flow [
23
]. Liu et al. (2017) confirmed that AVs improve the traffic stability and
increase the free-flow speed of the highway, mainly with higher penetration rates due to
smart maneuvers of AVs [
25
]. Stern et al. (2018) confirmed that the AV implementation,
even with lower penetration rates, could improve the stability by decreasing stop-and-go
waves in the traffic [51]. A single AV can substantially decrease those waves produced by
20 RVs around it in a circular road. Y. Zheng et al. (2020) concluded that AVs suppressed
unstable traffic waves and increase the speed of the flow [
52
]. The speed increased by
more than 6%, with only 5% AVs. F. Zheng et al. (2020) also confirmed that AVs improved
stability by decreasing speed variations in the traffic flow [
20
]. This improvement is
prominent with higher penetration rates of AVs. Fakhrmoosavi et al. (2020) concluded that
the implementation of CVs and AVs led to substantial improvements in traffic flow and
faster recovery [
7
]. In contrast to the above findings, Makridis et al. (2018) concluded that
the low penetration rate of AV had a negative impact on traffic stability [
43
]. The average
speed decreased while density and congestion increased with increased penetration rates
of AVs.

Sustainability 2021,13, 11052 12 of 22
5. Basic Factors Controlling Impacts of AV Implementation in a Mixed
Traffic Environment
In general, the obtained results of the different studies somehow leave a space to
dispute due to differences in their study types, tested AV type including their modeling
and settings, network level, traffic composition, traffic conditions, and driver behavior.
The relationship between the implementation of the AVs and their impacts on the mixed
traffic environment of AVs and RVs is complex and is based on different factors. Modeling
a mixed traffic requires countless and sophisticated interaction rules [
6
]. Some of the
studies discussed the importance of some examples of those factors. The impacts of AVs
on traffic stability are influenced by penetration rate, connection, platoon size, safe and
desired gap, and driver behavior [
20
]. According to Jin et al. (2020), the stability of mixed
traffic is dependent on the reaction time of drivers, the penetration rate of CAVs, and the
information that CAVs may collect [
46
]. Narayanan et al. (2020) mentioned that there are
19 related factors, including acceleration and deceleration profiles, lane-changing rules,
longitudinal driving behavior, presence of connectivity between AVs, desired gap, network
level, vehicle distribution in the network, implemented optimization function, the use of
dedicated lanes for CAVs and AVs, intersection control method, platoon size, and presence
of VSL [
1
]. On the other hand, F. Zheng et al. (2020) concluded that the reaction time and
the position of AVs are not considered as significant factors in terms of the AV’s impacts
on traffic stability. F. Zheng et al. (2020) concluded that there are no significant impacts of
CAV’s distribution on traffic stability [52].
This review has indicated that those discussed expected impacts of different types of
AV in mixed traffic environments are depending on some basic factors that may affect the
level and the magnitude of their impacts. These primary factors basically may include:
•Penetration rate of the AVs
•AV characteristics
•Modeling and operational settings of the AVs
•Traffic volume
•Human driving behavior in a mixed traffic environment
All these factors differ in their effects on the traffic flow impacts. Those factors,
including their effects, are discussed in the following section.
5.1. Penetration Rate of the AVs
Most of the studies have investigated the expected impacts of different AVs on the
mixed traffic environment using different penetration rates. For example, Van Arem et al.
(2006) confirmed that the penetration rate of CACC is only critical with high traffic volumes
scenarios and found that the critical rate that starts to indicate impacts is above 40% [
26
].
With low traffic volumes, there were no noticeable impacts of different penetration rates.
Schakel et al. (2010) concluded that when the penetration rate increased, the duration
of the shockwaves decreased, leading to improved stability [
38
]. Kesting et al. (2010)
concluded that with increasing rates of ACC in highway, the flow and the outflow from
traffic breakdowns increases [
32
]. Tientrakool et al. (2011) concluded that the capacity
increased slightly until the CACC penetration rate exceeded 85% [
27
]. Jerath and Brennan
(2012) concluded that low rates of ACC may lead to an increase in the congestion level [
33
].
Shladover et al. (2012) concluded that higher penetration rates of CACC are required
to have significant positive impacts on the traffic flow [
28
]. The positive impacts on the
stability and flow were noticed only when the penetration rate of CACC is above 40% [
14
].
The capacity increases as the penetration rate of AV increases [
9
]. Arnaout and Arnaout
(2014) confirmed no significant impacts of different penetrations rates at low volume traffic
scenarios. With high volume traffic scenarios, with penetration rates of less than 40%,
their impacts were minimal; however, when it is more than 40%, the increase in CACC
penetration rates leads to a significant increase in traffic flow. Ntousakis et al. (2015) also
confirmed that higher penetration of ACC led to improved stability [44]. Narayanan et al.
(2020) also supported that the traffic flow increases with the increase in the penetration

Sustainability 2021,13, 11052 13 of 22
rate of CAVs [
1
]. In addition, Talebpour and Mahmassani (2016) confirmed that higher
penetration rates of CAVs can increase the flow and capacity of the traffic network [
8
].
On the other hand, Calvert et al. (2017) concluded that the critical penetration rate above
70% of ACC that may start to indicate an improvement in traffic flow. The increase
of AV penetration rate improves the capacity, traffic stability, and free-flow speed [
25
].
The penetration rate of 50% is optimal in terms of lowering the congestion level. With the
use of a proper time gap, capacity increases with an increase in CAV penetration rate, and
it is more significant with rates higher than 30% [
4
]. When the penetration rate of CACC
is less than 20%, CACC has a negative impact on the traffic; however, when the rate is
more than 20%, it leads to an improvement [
31
]. Even low penetration rates of AVs, such as
5%, lead to positive impacts on traffic stability [
51
]. Compared to RVs, AVs have negative
impacts even at low penetration rates; however, higher rates, mainly in high volume traffic,
exhibit some improvement [
43
]. The improvement of traffic capacity and stability increases
with the increase in the penetration rate of CACC [
44
]. Lu et al. (2020) also confirmed
that the penetration rate of AVs in mixed traffic highly impacts the capacity, stability,
and flow. The capacity increase is high with 50% or more CAV penetration [
24
]. Using
the stochastic Lagrangian model, F. Zheng et al. (2019) concluded that the stop-and-go
traffic was decreased and the stability improved with an increasing penetration rate of
CAVs [
20
]. However, the penetration rates of 10% to 50% showed similar performance.
Higher penetration rates of CVs and AVs result in slightly higher flow and improved
stability [
7
]. F. Zheng et al. (2020) concluded that the improvement of the traffic stability is
more significant with high penetration rates (50%) of AVs [
52
]. Abdulsattar et al. (2020)
concluded that the capacity in the free-flow phase is directly proportional to the penetration
rate of CAVs. In congested flow, less than 40% rates did not show improvement in the
capacity [
13
]. The critical rate in terms of capacity improvement was found to be 50%.
Jin et al. (2020) found that when the penetration rate of CAVs goes up, stability improves,
and lead vehicle disturbance propagation fades away. Many studies confirmed that the
penetration rate of AV is highly an important factor that significantly affects the level of
their impacts [
46
]. According to some studies, they even mentioned that the penetration
rate is a critical factor only with high traffic volume conditions. However, few studies have
indicated that with low penetration rates, there is no expected improvement, or it may
even worsen the traffic conditions.
5.2. AV Characteristics
One of the important factors that highly affect the level of expected impacts of AV
implementation in a mixed traffic environment is the characteristics of the implemented
AV. Different AVs behave differently in the traffic flow due to their specific operational
characteristics, and their impacts vary mainly in a mixed traffic environment. The perfor-
mance of ACC systems depends only on the behavior of the leading vehicle. CACC or CAV
have advanced communication capabilities that gather and utilize more information of the
vehicles ahead or further downstream to automatically control the following longitudinal
behavior based on its leading and following vehicles. Unconnected AV’s conservative code
of conduct and inability to forecast nearby vehicle’s movements are the reasons for their
potential decreased capacity to produce favorable traffic flow impacts [1].
Some studies have directly compared the expected impacts of these different types
of AV under a mixed traffic environment. Compared to complete ACC traffic, complete
CACC traffic showed significantly improved capacity [
27
]. ACC did not show a significant
capacity improvement; however, CACC significantly improved the capacity [
28
]. CACC
implementation improves the stability of traffic flow compared to ACC [
54
]. Overcoming
the sensor constraints, CAVs can provide a smoother and more dependable driving expe-
rience than AVs [
8
]. CACC improves traffic stability while ACC causes instability of the
traffic flow [
44
]. Therefore, AVs are more significant than CVs in enhancing the traffic flow
and stability in the urban network [7].

Sustainability 2021,13, 11052 14 of 22
In general, it may be concluded that the implementation of CACC or CAV is better
than the non-connected AV or ACC in improving traffic capacity and stability. However,
some factors could affect their positive impacts, including penetration rate and their
distribution and position in the traffic flow and the type and the characteristics of the
leading vehicle. If the preceding vehicle of the connected vehicle is non-connected, it does
not positively impact the traffic flow. For example, the desired time headway may not
be assumed as a fixed number if the AV follows a RV. Moreover, CAV allows closer gaps
when following another CAV, but if it follows an RV or non-connected AV, its behavior
is same as these vehicles [
2
]. According to Makridis et al. (2018), CAVs that follow AVs
or RVs react as AVs, since they do not have any information from other vehicles to use
their connectivity capabilities [
43
]. At the lower rate of CAV penetration, the probability of
connected platoon formation of CAV is lower than the probability of a CAV following RV
or an AV [
43
]. It justifies the insignificant impacts of CAV at low penetration rates in mixed
traffic environments due to their required longer headways. However, the lower reaction
times of connected vehicles are achieved due to their radar detection systems [13].
On the other hand, the literature did not include a clear definition for these differ-
ent types of AVs. It was noticed that some studies assumed that AVs are connected to
other vehicles and have real-time information through V2I. For example, Talebpour and
Mahmassani (2016) stated that different studies sometimes use automation and connec-
tivity interchangeably [
8
]. Some studies did not specify the exact characteristics of the AV.
However, to have reliable and logical comparable results with other studies in terms of
evaluating the impacts of AVs, it is important to include a clear description of AV and
RV characteristics, including their selected driving models, operational settings, and their
connectivity capabilities with other objects in the traffic flow and other needed assumptions
for modeling the AVs. In the current literature, we have tried our best, with available
information in the studies, to specify the type of the used AVs within each study to have
clear conclusions about their impacts.
5.3. Modeling and Operational Settings of the AVs
Bailey (2016) confirmed that different behavioral models and parameters of the AV
may have different impacts on traffic flow. Some of the studies are discussed in the
following subsection.
5.3.1. Modeling of AVs
Several driving models have been proposed in the literature for modeling differ-
ent types of AVs for simulation works. Examples include the Intelligent Driver Model
(IDM), Cooperative IDM, Gipps driver model, and different acceleration frameworks.
Ntousakis et al. (2015) and Gora et al. (2020) in two different studies conducted a review on
the existing microscopic modeling of ACC [34,55]. In general, the specific driving models
of AVs involve two main groups: car-following and lane-changing models. The selection
of car-following models is more critical than the lane-changing models. Liu et al. (2017)
concluded that the car-following maneuvers of AVs confirmed contribute more to the
improvement than lane changing. Lane-changing in traffic flow usually leads to longer
time headways in the origin and destination lanes [
25
]. A vehicle requires a sufficient gap
in the destination lane and leaving a gap in the origin lane. As a result, more lane changes
could lead to negative impacts on the capacity. In terms of lane-changing modeling of
AVs, few studies investigated the impacts of lane-changing behavior in a mixed traffic
environment. The adjusted lane-changing behavior may cause some impacts on the traffic
flow. Makridis et al. (2018) concluded that smoother lane changes of AVs improve capacity
and stability [43].
In contrast, Liu et al. (2017) concluded that using different lane-changing rules of AVs
had negligible impacts on traffic flow [
25
]. Calvert et al. (2017) concluded that the impacts
of lane-changing modeling of ACC, such as fewer lane changes, do not show significant
impacts [22].

Sustainability 2021,13, 11052 15 of 22
5.3.2. Operational Settings of the AV
Each type of the AVs has a unique adjusted operational setting that controls its driving
behavior and, therefore, differently affect its expected impacts on the traffic flow characteris-
tics [
56
]. The selection of operational settings of car-following models of AV is an important
factor. Hoogendoorn et al. (2014) concluded that the expected impacts of AVs are highly af-
fected by the time headway and speed choice [
23
]. Li et al. (2017) also confirmed that ACC
performance is highly affected by the parameter settings. Inappropriate parameter settings
may increase the collision risks and therefore cause traffic instability [
35
]. However, the
optimal parameters are expected to be different for different traffic conditions. The safe and
desired gap settings and maximum platoon length are important factors for the expected
capacity improvement due to CACC implementation [
31
]. Kerner (2020) concluded that the
possibility of traffic instability is highly dependent on the dynamic parameters of ACC [
36
].
A few critical car-following settings include time and space headways, acceleration and
deceleration rates, speed, and reaction time.
Different studies have indicated that the most important settings of AV are the settings
of headway parameters. VanderWerf et al. (2004) concluded that a critical time gap of
1.4 sec is the limit between the deterioration and the improvement of the traffic flow;
however, that is valid only for when the penetration rate of CACC is higher than 60% [
37
].
Ntousakis et al. (2015) tested different time gap settings and concluded that the capacity
improvement of mixed traffic of ACC and RV is highly dependent on these settings [
34
].
With time gaps of less than 1.1 sec, the capacity increased linearly with the increase in the
penetration rate of ACC; however, with time gaps of 1.5 sec or more, it decreased even
when ACC penetration rate increased. Talebpour and Mahmassani (2016) also confirmed
that CAVs with shorter headway increase the throughput and capacity mainly with higher
penetration rates [8].
In contrast, the increased time headway has some negative impacts such as reduced
capacity and increased delays; however, it may improve the traffic stability [
4
,
8
,
35
].
Calvert et al. (2017) also confirmed that with the partial implementation of ACC [
22
].
On the other hand, Ye and Yamamoto (2018) concluded that CAV’s capability to maintain
shorter headways is negligible at lower penetration rates [
4
]. Mena-Oreja et al. (2018) tested
different time gaps settings in a complete CACC environment. They found that with the
use of desired gaps of 10 m, 5 m, and 1 m, the flow increases by 9.39%, 26.09%, and 39.21%,
respectively, compared to a complete RV environment [
31
]. Longer time gaps reduce speed
dispersion and improve traffic stability and safety; however, they may decrease capacity [
1
].
Kerner (2020) confirmed that the possible large speed disturbance of ACC traffic results
from their wide range of the long-desired time headway to the preceding vehicle [36].
Another critical aspect of the car-following driving models is the settings related to
AV’s speed distributions and acceleration/deceleration rates. For example, Le Vine et al.
(2015) investigated the impacts of CAV at a signalized intersection and concluded that
CAVs the operated with smoother acceleration/deceleration profile have negative impacts
on capacity because of the restriction on the dynamics of the vehicles [
41
]. Li et al. (2017)
also confirmed that higher deceleration rates improve stability and safety; however, that
may decrease the capacity [
35
]. According to Ye et al. (2018), the smoother traffic flow
with rising CAV penetration rate is attributable to a decrease in the percentage of high
deceleration rate [
42
]. The negative impacts of the low implementation rates of the AV are
due to their longer headways and lower maximum deceleration and acceleration compared
to the RVs [43].
In general, the exact driving behavior of different types of AVs in mixed traffic environ-
ments is complex and still not understood [
56
]. Most of the studies are based on different
assumptions of the driving behavior of AVs, such as car-following and lane-changing be-
havior. Some technical components regarding the modeling and simulation of AVs highly
affect its obtained expected impacts indicated by these different studies. Zeidler et al. (2019)
stated no common agreement or recommendations on parameter settings when modeling
AVs in micro-simulation software [
12
]. Even with the same type of AVs, the different

Sustainability 2021,13, 11052 16 of 22
applied operational settings, mainly those related to time headways, may highly affect
the traffic flow. The AV with shorter headways will lead to an increase in road capacity.
That is confirmed by the inversely proportional relationship between the capacity and
the minimum average time headway between the vehicles. However, some studies have
concluded that short headways have negative impacts on traffic stability. So, the settings of
the AV for the simulation are critical to explain or verify the different obtained findings.
5.4. Traffic Volume
Some studies have indicated that the traffic volume level affects the impacts of the
different AVs in a mixed traffic environment. The significant impacts of the AVs will be
clear only at high traffic volume scenarios. Van Arem et al. (2006) concluded that at low
traffic volumes, there were no significant impacts of different penetrations rates of CACC
on the traffic stability [
26
]. However, at high traffic flows, their impacts were significant.
Arnaout and Arnaout (2014) also confirmed this in terms of improving the traffic flow
due to CACC implementation [
40
]. Bohm and Häger (2015) confirmed that for a high
traffic volume, complete AV implementation increased the capacity [
29
]. However, for
low volumes, AVs deteriorated the traffic characteristics. Maurer et al. (2016) confirmed
the increased capacity of signalized intersection due to the AV implementation in case
of high traffic volumes, while the low traffic volumes indicated no improvement [
30
].
Makridis et al. (2018) also concluded that when the traffic volume was high, the impacts
of CAVs were positive; however, when the volume is low, they have found that the RVs
outperformed CAVs [43].
5.5. Human Driving Behavior in a Mixed Traffic Environment
As indicated earlier, it is known that the human driving behavior of the RVs and the
automated driving behavior of the AVs are not the same. The driving behavior of RVs can
vary depending on whether they are following another RV or a CAV [
57
]. Each has different
driving models and logic. For example, with human driving, the acceptable safe time gap
is determined based on the human’s perception and reaction time that is highly affected by
different factors such as the driver’s experiences, vision abilities, and expectations about
the behaviors of other vehicles including different types of AVs. Another example is that
human drivers may take risks while driving, trying to predict the movement of nearby
vehicles in the entire traffic stream in addition to the immediate vehicle [8,43]. It is highly
expected that the implementation of AVs in the mixed traffic environment will cause some
impacts on the driving behavior of RVs as a result of their interactions. However, the
literature has indicated that there are still uncertainties regarding these impacts that may
be justified by the limitation of the real data and complexity of the reliable simulation and
modeling of this complex traffic environment. From the available reviewed related studies,
it can be concluded that the human driving behavior of RVs, including their reactions and
adaptation, may affect the expected impacts of AV implementation. Schakel et al. (2010)
concluded that the increased headway variability, caused by mixed traffic of CACC and
RVs, had negligible impacts on the traffic stability [
38
]. Jerath and Brennan (2012) concluded
that any increase of the RV rate in mixed traffic with ACC leads to higher susceptibility
of congestion [
33
]. Maurer et al. (2016) concluded that a single RV in the traffic lane may
lead to slower speeds and decrease the improved capacity of the AV implementation [30].
On the other hand, high rates of AVs decrease the uncertainty of the RV behavior mainly
in congested traffic conditions compared to free flow, where the heterogeneous behavior
impacts are more significant due to increased mean speed variance [
52
]. The average
lane change of RVs increases until 30% penetration rate of CAVs is reached. After that, it
decreases as CAVs penetration rate increases [
45
]. Jin et al. (2020) concluded that one of the
main factors that may affect the impacts of CAV in mixed traffic is the driver ’s reaction
delay, which may cause traffic instability [
46
]. Kerner (2020) concluded that if the dynamics
of the AVs are qualitatively different from those of RVs, the AV decreases the capacity [
36
].

Sustainability 2021,13, 11052 17 of 22
6. Policy Implications
The implementation of the AV will transform transportation systems and traffic flow
characteristics depending on many associated factors. Eliminating human errors and
providing real-time information on vehicles from the connected infrastructure and other
vehicles can result in significant driving behavioral improvements. Many policymakers
wonder how AVs will change the future traffic demands, and how that may affect the
planning for different transportation infrastructures such as roads, parking, and public
transportation systems, and whether strategies and laws should encourage or limit their
implementation [
58
]. In order to formulate policies, the first step is to identify problems
that need to be addressed, determine which issues require the greatest attention, and
specify what the nature of the problem is [59].
The existing traffic environment of road networks mainly includes RVs and some
different types of semi-autonomous vehicles with different driving assisting systems.
However, in the coming future, it is expected that the penetration rate of different types
of AVs, including the full AV level, in the traffic environment will increase and therefore,
it will cause different impacts on the traffic systems. The findings of this review have
confirmed these related impacts of its implementation on the traffic capacity and the
components of traffic stability. Furthermore, it has confirmed some critical related factors
that control the level and magnitude of the expected impacts. As a result, the traffic
authorities and engineers need to consider the specific characteristics of different AV types
and their expected impacts under different future traffic compositions to propose effective
policies for future traffic operation and control. The primary outcomes of the included
discussions in this research may be significantly utilized to achieve that. These policies are
related to optimized AV driving parameters, such as desired speed and headway, under
different traffic environment compositions or network levels. In addition, other essential
policies can be related to the design or optimization of the traffic control systems, such as
signalized intersection control under a shared environment of AVs and RVs. Such policies
are critical to ensure traffic efficiency and safety in the actual practice of the AV traffic
environment [60].
The findings indicated that the complete implementation of different AVs significantly
increases the traffic capacity compared to a complete RV traffic environment. None of the
studies have shown negative impacts on traffic stability under a complete AV environment;
however, different studies logically assumed that full stability will be achieved. In the case
of complete AV traffic, the desired driving parameters of the AVs can be controlled and
optimized to achieve the optimum traffic flow parameters. The presence of connectivity
between AVs is a very critical aspect of modeling. The implementation of CAVs enhances
the efficiency and reliability of AV performance.
Due to different factors, it may take a very long time to reach a 100% implementation of
the AV in our traffic networks. The findings have indicated that the partial implementation
of the AVs has different impacts on the traffic flow characteristics. Some of the studies have
indicated that it may lead to different levels of improvement and others have conflicted
that due to different factors. The related investigation works have considered different AV
types, different car-following models, and different operational driving settings. In general,
ACC implementation increases traffic capacity and improves traffic flow stability, mainly
with higher penetration rates.
In contrast, some other studies have indicated that ACC has negative impacts on
capacity or stability. In terms of the CACC or CAV impacts, in general, most of the studies
have also confirmed that their implementation may improve the capacity and stability
mainly with higher penetration rates. In contrast, some studies have indicated that CACC
or CAV negatively impact the capacity or stability when their penetration rate is low. Two
reviewed studies by Narayanan et al. (2020) and the authors of [
4
] indicated negative
impacts of CAV on traffic capacity at any rate. In terms of the AV impacts, in general, most
of the studies have also confirmed that the AV may increase the capacity and improve the
stability mainly with higher penetration rates. However, only one study concluded that the

Sustainability 2021,13, 11052 18 of 22
AVs may have negative impacts on traffic stability even with higher penetration rates [
43
].
None of the studies have indicated negative impacts of AVs on the capacity of a mixed
traffic environment.
So, as indicated earlier, due to their improved operational characteristics, the im-
plementation of AVs may offer several potential positive impacts that may improve the
overall efficiency of traffic mobility. However, these expected impacts are based on some
crucial factors that should be considered while evaluating the AV’s impacts on the traffic
flow under a mixed traffic environment. This is highly important to maximize its positive
impacts with the complex interaction with RVs. These factors are mainly related to their
penetration rate, AV characteristics, modeling and operational settings of the AVs, and the
level of the traffic volume, in addition to the impacts of human driving behavior.
The penetration rate of the AVs is the most critical factor that may affect its expected
impacts in a mixed traffic environment. Most of the studies have confirmed that the level
of the expected improvement increases as the penetration rate increase. According to this
review, the critical penetration rate should be above 40% to have significant positive impacts
on the traffic capacity and stability. The policymakers should encourage and support the
implementation of AVs in traffic networks by proposing different strategies mainly in cities
with high traffic density. Moreover, the connectivity capabilities of the AVs can highly affect
the level and the magnitude of the expected improvement on the traffic flow mainly in
the case of a mixed traffic environment. The review has indicated that the implementation
of CAVs is better than the non-connected AVs in terms of improvement of traffic capacity
and stability. This superiority is due to their significant advanced characteristics, such
as their ability to communicate and obtain more valued information about the traffic,
enabling smarter control of car-following and lane-changing maneuvers. That may benefit
the vehicles to maintain a better driving behavior by allowing shorter headways between
them, faster reaction times, and complex maneuvers, such as platooning and improved
merging or lane changing, with fewer disturbances to the traffic flow [
61
]. All of that can
result in more utilization and optimization of the road capacity. In contrast to the CAVs,
the behavior of the non-connected vehicles is governed by specific theories. For example,
the gap acceptance theory in which the driver has critical spacing to perform safe lane
merging [
2
]. Narayanan et al. (2020) concluded that the policymakers should enforce
laws to ensure connectivity between AVs to maintain significant positive impacts of the
AV implementation [
1
]. The findings also indicated other important aspects that should
be considered, while evaluating the impacts of different types of AVs is the position and
the distribution of the AVs in the traffic flow in addition to the type of the following or
leading vehicles of the AV. Most of the related studies in the literature have only focused on
the penetration rates of each type, ignoring these important factors. However, in order to
achieve the optimum policy for future traffic environment, it is highly important to conduct
a reliable investigation considering several possible combinations regarding the type of
each following or leading vehicle of an AV in the traffic flow considering different types of
AVs in addition to the RVs.
On the other hand, the selection of the driving models of the AVs and their operational
settings is also considered a critical factor of the expected impacts. Among them, these
impacts mostly resulted due to the adjustment of the car-following driving behavior mainly
the headways and speed settings. However, optimizing these settings is highly complex
since these may lead to different impacts on different flow characteristics. In addition, these
adjusted settings may cause several impacts on other aspects such as traffic safety and
driver comfort. So, the policymakers should realize that the optimum selection of these
parameters may depend on different factors such as the penetration rate of the AVs and the
network level and other targeted objectives of the AV implementation. However, in terms
of human driving behavior in a mixed traffic environment, the review has indicated that
the possible negative impacts of the AVs on the RV deriving behavior may be gradually
decreased with the increase of the AV’s rates. Moreover, as recommended by Kerner (2020),
future systems for AVs should be developed whose rules are consistent with those of RVs

Sustainability 2021,13, 11052 19 of 22
in which both the car-following and lane-changing models should learn from the driver
behavior of the RV [36].
7. Conclusions
This paper included a comprehensive literature review to investigate the AV implementa-
tion impacts on the traffic flow characteristics under a mixed traffic environment with RVs. It
summarized the main findings of the reviewed studies considering their specific study variables,
including the AV’s type, network type, and the study type. In addition, the paper discussed
important related factors that may affect the level of the impacts.
The review has shown that most of the related studies focused on investigating the
possible impacts of the complete implementation of the AVs. Few studies have considered
the issue of its expected impacts under a mixed traffic environment. Instead, most of the
studies have focused on the impacts of the partial automation levels of AVs (ACC, CACC).
In general, the complete or partial implementation of AVs offers several potential pos-
itive impacts that may improve the overall efficiency of the traffic system. However, there
are contradictory results about their expected impacts on the traffic flow characteristics
in terms of mixed traffic environment. Different studies indicated that compared to the
complete environment of RVs, the partial implementation of different types of AVs, mainly
the CAV, increase the traffic capacity, improve the traffic flow stability, increase through-
put, and decrease the probability of traffic breakdown, including shockwave formation
and propagation, and therefore decrease the intensity and quantity of congestion waves.
However, those are based on some important factors that should be considered, including
penetration rate, AV characteristics, modeling and operational settings of the AV, and the
level of the traffic volume in addition to the impacts of human driving behavior.
Based on the review’s main findings, the paper included a policy implication that in-
tended to provide guidance on the interpretation of the main obtained results for the actual
future practice and further research interests. In addition, it was concluded that the actual
impacts of the implementation of the AV on mixed traffic still need more investigation.
•
There are uncertainties about the impacts of the full level of AVs, including CAVs,
on the traffic flow under mixed traffic conditions. Therefore, further investigation
efforts are highly needed under various urban networks levels, including different
types of signalized and non-signalized intersections. Additionally, it is recommended
to consider various traffic compositions, including different combinations of different
types of AVs and RVs.
•
In terms of AV modeling, most of the reviewed studies did not consider lane-changing
behavior. Therefore, further investigation efforts are needed to evaluate the impacts of
lane-changing modeling of different types of AV under a mixed traffic environment.
•
The review indicated that still further investigations are needed on human driving
behavior under mixed traffic environments considering different types of AVs.
Author Contributions:
Conceptualization, M.A.-T. and N.T.R.; methodology, M.A.-T. and I.R.; soft-
ware, M.A.-T.; validation, M.A.-T.; formal analysis, M.A.-T.; investigation, N.T.R., M.A.-T., and S.M.R.;
resources, M.A.-T. and I.R.; data curation, M.A.-T. and S.M.R.; writing—original draft preparation,
M.A.-T.; writing—review and editing, M.A.-T., S.M.R., and I.R.; visualization, N.T.R., M.A.-T., and
S.M.R.; supervision, N.T.R. and M.A.-T.; project administration, N.T.R. All authors have read and
agreed to the published version of the manuscript.
Funding: This research received no external funding.
Acknowledgments:
The authors would like to gratefully acknowledge the support of King Fahd
University of Petroleum & Minerals (KFUPM) in conducting this research.
Conflicts of Interest: The authors declare no conflict of interest.

Sustainability 2021,13, 11052 20 of 22
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