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Adoption of Autonomous Vehicles with Endogenous Safety Concerns: A Recursive Bivariate Ordered Probit Model

Authors:
Fatemeh-Noosheen Nazari at University of Texas Rio Grande Valley
Fatemeh-Noosheen Nazari
Mohamadhossein Noruzoliaee at University of Texas Rio Grande Valley
Mohamadhossein Noruzoliaee
Abolfazl Mohammadian at University of Illinois at Chicago
Abolfazl Mohammadian
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Abstract and Figures

It is generally believed that safer roads will be a major benefit of autonomous vehicles (AVs). Recent AV-involved accidents in road tests and empirical evidence, however, suggest that travelers might still be concerned about the safety of a ride in an AV. It is further unknown whether and to what extent the travelers’ safety concern will hinder or promote the adoption of AVs. This paper makes a rigorous attempt to ascertain the causality between the travelers’ safety concern about the AV technology and their AV adoption behavior, in addition to exploring the determinants thereof. To this end, a recursive bivariate ordered probit model is estimated, which explicitly accounts for the endogeneity of safety concern in the AV adoption behavior. Drawing from a stated preference survey in the state of California, the authors find a significant negative association between safety concern and AV adoption. Important insights are also obtained into the impact on shaping travelers’ behavior of several socioeconomic and demographic characteristics, current travel behavior factors, and vehicle decision factors and attributes.
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Adoption of Autonomous Vehicles with Endogenous Safety Concerns:
A Recursive Bivariate Ordered Probit Model
Fatemeh Nazari (Corresponding author)
Ph.D. Student
Department of Civil and Materials Engineering
University of Illinois at Chicago
842 W. Taylor Street
Chicago, IL 60607-7023
Email: fnazar2@uic.edu
Mohamadhossein Noruzoliaee
Ph.D. Candidate
Department of Civil and Materials Engineering
University of Illinois at Chicago
842 W. Taylor Street
Chicago, IL 60607-7023
Email: mnoruz2@uic.edu
Abolfazl (Kouros) Mohammadian
Professor
Department of Civil and Materials Engineering
University of Illinois at Chicago
842 W. Taylor Street
Chicago, IL 60607-7023
Email: Kouros@uic.edu
Paper number 19-02998 (Extended Abstract)
98th Annual Meeting of Transportation Research Board (TRB)
Word Count: 1,621
Nazari, Noruzoliaee, and Mohammadian 1
ABSTRACT
It is generally believed that safer roads will be a major benefit of autonomous vehicles (AVs). Recent AV-
involved accidents in road tests and empirical evidence, however, suggest that travelers might still be
concerned about the safety of a ride in an AV. It is further unknown whether and to what extent the travelers’
safety concern will hinder or promote the adoption of AVs. This paper makes a rigorous attempt to ascertain
the causality between the travelers’ safety concern about the AV technology and their AV adoption
behavior, in addition to exploring the determinants thereof. To this end, a recursive bivariate ordered probit
model is estimated, which explicitly accounts for the endogeneity of safety concern in the AV adoption
behavior. Drawing from a stated preference survey in the state of California, we find a significant negative
association between safety concern and AV adoption. Important insights are also obtained into the impact
on shaping travelers’ behavior of several socioeconomic and demographic characteristics, current travel
behavior factors, and vehicle decision factors and attributes.
Keywords: autonomous vehicle, adoption behavior, safety concern, endogeneity, recursive bivariate
ordered probit
Nazari, Noruzoliaee, and Mohammadian 2
INTRODUCTION
1
It is envisaged that the emergence of autonomous vehicles (AVs) will transform transportation systems
2
through more efficient mobility and enhanced safety. In particular, AVs could remove the leading cause of
3
road crashes, which is human error in 90% of the U.S. road accidents (1). Even without full automation,
4
the economic benefits of partially automated vehicle collision avoidance technologies in the U.S. are
5
projected at up to $202 billion (2). Notwithstanding, the recent AV crashes in road tests (e.g., see (3)) could
6
cast doubts on the reliability of future transport safety with AVs in terms of fatalities and injuries. Such a
7
blurred picture of future road safety is even exacerbated by noting that car manufacturers and decision
8
makers cannot simply prove AV reliability through extensive road tests (4). As a consequence, consumers’
9
perceptions about the safety of a ride in an AV could be negatively affected (5-7). Despite the potentially
10
significant impact of consumers’ safety concern on their adoption of AVs, there is a dearth of behavioral
11
studies to explore the causality between the travelers’ safety concern about the AV technology and their
12
AV adoption behavior (8).
13
However, existing travel behavior studies on AVs mostly ignore safety concern about the AV
14
technology and mainly focus on socio-economic, built-environment, current travel behavior, and
15
instrumental variables. A main hypothesis in this paper is that AV adoption is controlled by, among various
16
factors, the safety concern of travelers, which is itself a function of exogenous factors. For instance, persons
17
who are more familiar with new technologies, especially vehicle technology, could be expected to have
18
lower concerns about AV safety and thus be more interested in AV adoption. In other words, we
19
simultaneously model AV adoption and safety concern while considering the endogeneity between the two
20
dependent variables. To do so, we estimate a recursive bivariate ordered probit (RBOP) model. In addition
21
to treating endogeneity, the model captures the cross-equation error correlation between the two dependent
22
variables. Ignoring the endogeneity could lead to inconsistent parameter estimates, inaccurate predictions,
23
and erroneous inferences (9).
24
25
METHODOLOGY
26
To address endogeneity in a bivariate probit model, Burnett (10) proposed the recursive bivariate probit
27
model, which jointly models two outcomes while addressing variable endogeneity. Later on, Sajaia (11)
28
extended the recursive bivariate probit to jointly model two ordinal outcomes while addressing endogeneity.
29
In this paper we estimate a recursive bivariate ordered probit model to simultaneously model AV adoption
30
and AV safety addressing endogeneity and cross-equation correlation of error terms.
31
32
FINDINGS
33
The majority of the existing studies on AV adoption behavior collect stated preference data sets, in which
34
respondents choose one of the alternatives presented as possible scenarios (see Becker and Axhausen (8)
35
for a recent review of these studies). Each scenario in a stated preferences survey presents one alternative
36
(e.g., AV, shared AV, and conventional vehicle) with specific features and a respondent could choose one
37
or multiple options. In this paper, we estimate a RBOP mode using the stated preference data set provided
38
by California Energy Commission (12), which does not contain features of AVs, but the respondents are
39
asked about their agreement level with AV adoption and AV safety. Specifically, the respondents answered
40
the following two questions by Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree).
41
42
• AV adoption: “I would consider purchasing a vehicle that is fully self-driving (i.e., the vehicle drives
43
itself)”.
44
• AV safety concern: “I am concerned about the safety of self-driving vehicles”.
45
46
We assume that “considering AV as an option for future vehicle decision” could represent potential
47
for adopting an AV in the future. The data set contains Californians’ response to the mentioned questions
48
from five regions of California: San Francisco, Los Angles, San Diego, Sacramento, Central Valley, and
49
the rest of California. Based on the residential region of the respondents, the distribution of the response
50
Nazari, Noruzoliaee, and Mohammadian 3
variables is depicted in FIGURE 1. It is interesting to note that almost half of the respondents in all five
1
regions and the rest of California strongly or moderately disagree with adopting an AV. In addition, majority
2
of them strongly or moderately agree on safety concerns about AVs. Among the five regions, the residents
3
of San Francisco showed more agreement with AV adoption and more disagreement with its safety
4
concerns.
5
6
(a)
(b)
FIGURE 1. Response of the five regions of the state of California to: (a) AV adoption and (b) AV safety
7
concern (note: the numbers in the parenthesis are the number of observations for the
8
corresponding region)
9
10
0
5
10
15
20
25
30
35
40
45
50
San Francisco
(843) Los Angeles
(1,502) San Diego
(339) Sacramento
(273) Central Valley
(306) Rest of CA
(311)
Share of respondents (%)
Strongly disagree Moderately disagreeNeither agree nor disagree
Moderately agreeStrongly agree
0
5
10
15
20
25
30
35
40
45
50
San Francisco
(843) Los Angeles
(1,502) San Diego
(339) Sacramento
(273) Central Valley
(306) Rest of CA
(311)
Share of respondents (%)
Strongly disagree Moderately disagreeNeither agree nor disagree
Moderately agreeStrongly agree
Nazari, Noruzoliaee, and Mohammadian 4
TABLE 1 shows the estimation results of the recursive bivariate ordered probit model of people’s AV
1
adoption with endogenous AV safety while accounting for cross-equation correlation. The estimated model
2
has a fair prediction accuracy (indicated by R2 = 0.21). In addition, the ordered levels of both dependent
3
variables are separated by significant estimated thresholds.
4
5
TABLE 1. Estimation results of recursive bivariate ordered probit model
6
Variable description
AV adoption
AV safety concern
coef.
t-stat
coef.
t-stat
Constant
3.703
11.34
2.414
18.48
Endogenous variable
AV safety
-0.644
-8.79
—
—
Socio-economic characteristics
Gender
Female
-0.110
-2.62
0.156
4.14
Education
Level 2
—
—
-0.144
-1.83
Level 3
—
—
-0.205
-2.64
Level 4
—
—
-0.268
-3.39
Employment type
Full-time employed
0.065
1.70
—
—
Self-employed
0.107
1.50
—
—
Household income
Low level (less than 75K)
-0.086
-2.20
—
—
High level (equal or more than 200K)
0.191
3.02
—
—
Household structure
# kids (age < 6) and teenagers (6 ≤ age < 12)
0.083
3.25
—
—
# young children (12 ≤ age < 16)
0.139
2.85
—
—
# vehicles per # adults (in household)
-0.181
-4.54
—
—
Household has plug-in electric vehicle
Yes = 1
—
—
-0.468
-7.50
Household has/ plans to purchase solar panels
Yes = 1
—
—
-0.082
-2.01
Demographic factor
Residential region
San Francisco
0.190
4.01
—
—
Log Angeles
0.069
1.76
—
—
Travel behavior factors
Logarithm of annual VMT (individual-level)
-0.037
-3.49
-0.031
-2.88
Use of mobility-on-demand services
Car-sharing frequency
—
—
-0.064
-2.99
Ride-sharing frequency
—
—
-0.046
-3.69
Daily parking cost at residence (× 10-3)
0.724
2.54
—
—
Vehicle decision factors
Important attributes of a vehicle
Reliability
—
—
0.081
2.30
Brand
—
—
0.097
2.32
Vehicle history of household in the past 10 years
# vehicles purchased new
0.028
1.99
—
—
Nazari, Noruzoliaee, and Mohammadian 5
Variable description
AV adoption
AV safety concern
coef.
t-stat
coef.
t-stat
# vehicles leased
0.046
2.58
—
—
Involvement in future vehicle decisions
Sole decision maker
0.140
3.20
—
—
Shared equally with other household member(s)
-0.082
-1.76
—
—
Logarithm of price for replacing one of vehicles
-0.014
-1.49
—
—
Logarithm of price for adding a vehicle
0.014
3.59
—
—
Error correlations
AV adoption
1.00
—
0.394
3.84
AV safety concerns
1.00
—
Thresholds
Threshold 1
0.00*
—
0.00*
—
Threshold 2
0.486
18.88
0.504
14.55
Threshold 3
1.040
23.10
1.051
24.85
Threshold 4
1.735
25.49
1.953
45.06
Goodness-of-fit measures
No. of observations = 3,574
𝐿𝐿(𝛽) = -9,877, 𝐿𝐿(0) = -12,528, R2 = 0.21
*Threshold 1 is fixed at zero.
1
2
The estimated model captures the cross-equation error correlations, which appropriately absorb any
3
propensity for AV adoption and AV safety associated with omitted exogenous variables (or unobserved
4
factors). We find significant and positive correlations (𝜌 = 0.394) across the error components of the
5
equations which suggests same-sign association of the outcomes with the omitted exogenous variables. It
6
should be noted that the correlation between the two dependent variables, AV adoption and AV safety, is
7
negative. In fact, the cross-equation error correlation is different from the correlation between the two
8
outcome variables; the former captures the unobserved heterogeneity in the error terms, while the latter
9
shows the linear association between the two variables.
10
To test the hypothesis of zero correlation of the error terms (𝜌 = 0), we use the likelihood ratio test
11
by comparing the estimated model with a restricted model which corresponds to independent ordered
12
response estimation of each of the two outcomes (13). The likelihood ratio test with p-value << 0.0001
13
shows that in this particular empirical context, it cannot be rejected to model AV adoption and AV safety
14
accounting for the correlation across the error components of the equations.
15
Almost all estimated coefficients are statistically significant at the 0.05 level and intuitively signed.
16
The sign of each estimated coefficient is of particular interest: a positive sign means increase in the highest
17
agreement level (i.e., strongly agree) or decrease in the lowest disagreement level (i.e., strongly disagree)
18
of AV adoption and AV safety (13). However, analysis of the intermediate order levels of an ordered probit
19
model (i.e., the three middle agreement levels in this model) requires computing the associated marginal
20
effects, as illustrated in the methodology section. TABLE 2 presents the marginal effects of the exogenous
21
variables explaining each level of agreement with AV adoption and AV safety, which refer to the
22
approximate change in the probability of each agreement level with AV adoption and AV safety in response
23
to a unit change in the desired exogenous variable while other variables are held constant at their respective
24
population mean.
25
The estimated model further accounts for endogeneity of AV safety in the equation of AV adoption,
26
which is signified by the negative coefficient of AV safety in the equation of AV adoption. In fact, as a
27
person disagrees more with AV safety, he/she agrees less, strongly or moderately, with AV adoption. To
28
test the hypothesis of no endogeneity, we use the likelihood ratio test by comparing the estimated model
29
with a restricted model which corresponds to bivariate ordered probit model with no endogeneity (13). The
30
Nazari, Noruzoliaee, and Mohammadian 6
likelihood ratio test with p-value < 0.05 shows that in this particular empirical context, it cannot be rejected
1
to jointly model AV adoption and AV safety without endogenous AV safety.
2
3
TABLE 2. Marginal effects for recursive bivariate ordered probit model
4
Variable description
AV adoption
AV safety concern
Strongly
disagree
Moderately
disagree
Neither agree
nor disagree
Moderately
agree
Strongly
agree
Strongly
disagree
Moderately
disagree
Neither agree
nor disagree
Moderately
agree
Strongly
agree
Endogenous variable
AV safety
0.190
0.019
-0.019
-0.060
-0.131
—
—
—
—
—
Socio-economic characteristics
Gender
Female
0.033
0.003
-0.003
-0.010
-0.022
-0.013
-0.014
-0.019
-0.013
0.060
Education
Level 2
—
—
—
—
—
0.012
0.013
0.017
0.012
-0.055
Level 3
—
—
—
—
—
0.017
0.019
0.025
0.018
-0.079
Level 4
—
—
—
—
—
0.023
0.024
0.032
0.023
-0.103
Employment type
Full-time employed
-0.019
-0.002
0.002
0.006
0.007
—
—
—
—
—
Self-employed
-0.032
-0.003
0.003
0.010
0.022
—
—
—
—
—
Household income
Low level (less than 75K)
0.026
0.002
-0.002
-0.008
-0.018
—
—
—
—
—
High level (equal or more than 200K)
-0.056
-0.006
0.006
0.018
0.039
—
—
—
—
—
Household structure
# kids (age < 6) and teenagers (6 ≤ age < 12)
-0.024
-0.002
0.002
0.008
0.017
—
—
—
—
—
# young children (12 ≤ age < 16)
-0.041
-0.004
0.004
0.013
0.028
—
—
—
—
—
# vehicles per # adults (in household)
0.053
0.005
-0.005
-0.017
-0.037
—
—
—
—
—
Household has plug-in electric vehicle
Yes = 1
—
—
—
—
—
0.040
0.042
0.057
0.040
-0.179
Household has/plans to purchase solar panels
Yes = 1
—
—
—
—
—
0.007
0.007
0.010
0.007
-0.031
Demographic factor
Residential region
San Francisco
-0.056
-0.006
0.005
0.018
0.039
—
—
—
—
—
Log Angeles
-0.020
-0.002
0.002
0.006
0.014
—
—
—
—
—
Travel behavior factors
Logarithm of annual VMT (individual-level)
0.011
0.001
-0.001
-0.003
-0.007
0.003
0.003
0.004
0.003
-0.012
Use of mobility-on-demand services
Car-sharing frequency
—
—
—
—
—
0.005
0.006
0.008
0.006
-0.024
Ride-sharing frequency
—
—
—
—
—
0.004
0.004
0.006
0.004
-0.017
Daily parking cost at residence (× 10-2)
-0.214
-0.021
0.021
0.067
0.147
—
—
—
—
—
Vehicle decision factors
Important attributes of a vehicle
Reliability
—
—
—
—
—
-0.007
-0.007
-0.010
-0.007
0.031
Brand
—
—
—
—
—
-0.008
-0.009
-0.012
-0.008
0.037
Vehicle history of household in the past 10 years
Nazari, Noruzoliaee, and Mohammadian 7
Variable description
AV adoption
AV safety concern
Strongly
disagree
Moderately
disagree
Neither agree
nor disagree
Moderately
agree
Strongly
agree
Strongly
disagree
Moderately
disagree
Neither agree
nor disagree
Moderately
agree
Strongly
agree
# vehicles purchased new
-0.008
-0.001
0.001
0.003
0.006
—
—
—
—
—
# vehicles leased
-0.014
-0.001
0.001
0.004
0.009
—
—
—
—
—
Involvement in future vehicle decisions
Sole decision maker
-0.041
-0.004
0.004
0.013
0.028
—
—
—
—
—
Shared equally with other household member(s)
0.024
0.002
-0.002
-0.008
-0.017
—
—
—
—
—
Logarithm of price for replacing one of vehicles
0.004
0.0004
-0.0004
-0.001
-0.003
—
—
—
—
—
Logarithm of price for adding a vehicle
-0.004
-0.0004
0.0004
0.002
0.003
—
—
—
—
—
1
CONCLUSIONS
2
A main hypothesis in this paper is that AV adoption is controlled by, among various factors, the
3
safety concern of travelers, which is itself a function of exogenous factors. For instance, persons who are
4
more familiar with new technologies, especially vehicle technology, could be expected to have lower
5
concerns about AV safety and thus be more interested in AV adoption. In light of this, we simultaneously
6
model AV adoption and safety concern while considering the endogeneity between the two dependent
7
variables. To do so, we estimate a recursive bivariate ordered probit (RBOP) model. In addition to treating
8
endogeneity, the model captures the cross-equation error correlation between the two dependent variables.
9
Drawing from a stated preference survey in the state of California, we find a significant negative association
10
between safety concern and AV adoption. Important insights are also obtained into the impact on shaping
11
travelers’ behavior of several socioeconomic and demographic characteristics, current travel behavior
12
factors, and vehicle decision factors and attributes.
13
14
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16
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... Accordingly, understanding the public's acceptance and potential adoption of SAVs is fundamental. Several studies have explored the potential users and riders of AVs by modeling their willingness to use and pay for this technology (Acheampong and Cugurullo 2019;Etzioni et al. 2021;Nazari et al. 2019;Shabanpour et al. 2018;Wang and Akar 2019;Yuen et al. 2020); however, the extent to which the research findings will coincide with reality when self-driving vehicles are on the road is still unclear. This study employs a nonsimulated, realworld environment based on an SAV pilot project called RAPID (Rideshare, Automation, and Payment Integration Demonstration) in the city of Arlington, Texas, which is a unique case study, as it is one of the largest cities in the United States without access to a fixed-route mass transit service (Harrington 2018). ...
... Several studies have been conducted to identify the public's willingness to use and pay for SAVs (Acheampong and Cugurullo 2019;Etzioni et al. 2021;Nazari et al. 2019;Shabanpour et al. 2018;Wang and Akar 2019;Yuen et al. 2020), and sociodemographic characteristics have been suggested as one of the main factors that shapes individuals' views and inclinations to avail themselves of self-driving technology. For example, females and older people are less likely to use driverless buses with onboard assistance operators than are well-educated males between the ages of 18 and 34 years old who earn above-average incomes and live in dense urban areas (Bansal et al. 2016;Lavieri et al. 2017;Lu et al. 2017;Wang and Akar 2019). ...
... Individuals' attitudes, preferences, concerns, and perceptions toward automated technology are constantly explored through AV and SAV literature, which has revealed that technically savvy people are more likely to have a positive attitude toward using an AV service that has been integrated into an existing public transit system (Song and Noyce 2019). On the other hand, safety concerns about AV can negatively influence its adoption (Nazari et al. 2019), as people who feel unsafe and uncomfortable riding transit are less likely to choose automated transit. Organized people who enjoy multitasking are more likely to choose automated transit over private vehicles (Etzioni et al. 2021), as are risk-takers, who are more likely to utilize them than are older individuals with risk-averse attitudes (Hulse et al. 2018). ...
A User and Ridership Evaluation of Shared Autonomous Vehicles
Article
Full-text available
  • Sep 2022
Cities around the world are piloting projects to evaluate the feasibility and benefits of shared autonomous vehicles (SAVs), as their large-scale implementation and integration into public transit systems have the potential to improve individuals’ accessibility and transportation equity. To understand the full potential of SAVs and their likely adoption, it is important to identify how the services can be utilized most effectively and what determines the composition of the ridership. This research aims to explore the usage and adoption of SAVs, focusing on a project called RAPID (Rideshare, Automation, and Payment Integration Demonstration) that was launched in Arlington, Texas. We used real-time trip-level ridership data from the SAV platform, conducted a survey of SAV riders, and developed a study based on ordered logistic regression to estimate the determinants of ridership frequency. Data analysis of real-time ridership data revealed that spatial distribution of activities and service accessibility have crucial roles in forming the current users’ travel patterns. The findings from the logistic regression demonstrated that those with higher household incomes are less likely to be frequent riders of RAPID, while those who usually walk, bike, or utilize on-demand ridesharing services are likely to use SAVs often. Users with higher levels of safety perception are also more likely to be frequent users of the service. The findings of this study will provide planners with a better understanding of SAV ridership patterns and will guide decision-makers nationwide in establishing and adopting policies that will be appropriate for future SAV implementation projects.
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... The long period of extremely cold weather that leads to longer periods of ice and snow coverage could raise a specific safety question for the people living in such areas. It is important to note that safety is still a challenging subject that can act as a discouraging factor for people in adopting AVs (Nazari et al., 2019). Precipitations such as snow, fog and hail have the potential of interfering with the localization of AVs (Ort et al., 2020) in addition to weakening their object recognition (Zang et al., 2019). ...
... Similarly, this variable increases the likelihood of disagreeing with the SPR for the model of students according to Table 8. This result for the respondents in the above 25 years old and student models resembled that in a study in larger metro areas in California where a higher ridesharing frequency was found to be associated with a decreased likelihood of being strongly concerned about AVs' safety of ride (Nazari et al., 2019). The ME of this variable on the likelihood of agreeing with WNT, given that SPU ¼ 1 and SPR ¼ 1, is 7% for the respondents in the above 25 years old model according to Table 9. ...
... Such respondents appear to agree with the WNT statement which can potentially pave their way for an earlier adoption of AVs. However, a low income has generally appeared to be negatively correlated with willingness to pay for AVs (Bansal et al., 2016; or with adopting AVs (Nazari et al., 2019) in larger and warmer metro areas. This result suggests given that the respondents of low income households acknowledge the stated benefits and agree with the stated risks of AVs, their opinion towards WNT variable and potentially towards the adoption of the AVs is positive. ...
The Attitudes towards Autonomous Vehicles in a Medium-Sized Academic-Dominated U.S. Metropolitan Area With Cold Winters
Article
  • May 2022
Autonomous vehicles, AVs, as an emerging technology will contribute to fundamental changes in the transportation of cities. Medium-sized academic-dominated U.S. towns with cold winters and a large proportion of college students and employees are some of the regions with special travel patterns that may potentially contribute to a different perception of AVs. Using a stated preference survey in the two main universities in the Fargo-Moorhead area, perceived usefulness, perceived risk, and a winter-related variable defined as transportation safety improvement by AVs in inclement weather were simultaneously modeled. Given that the respondents agreed with the stated usefulness and risks of AVs, not being single was associated with an increased likelihood of agreeing with the winter-related variable for the respondents above 25. Low income households were associated with a 25% increase in the likelihood of agreeing with the winter-related variable for the respondents in the non-student model.
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... Several studies have analyzed the factors impacting SAV adoption, focusing on their perceived benefits and ease of use, as well as the potential users' preferences, safety concerns, commuting behaviors, and attitudes toward using SAVs (e.g., Acheampong and Cugurullo 2019;Etzioni et al., 2021;Nazari et al., 2019;Shabanpour et al., 2018;Wang and Akar 2019;Yuen et al., 2020). The adoption of AVs not only depends on their functional elements and actual performance metrics, such as travel cost, travel time, speed, and safety, but also on potential users' attitudes and perceptions toward the technology. ...
... The results of this study show that people in the northeast section of the U.S. have more positive attitudes toward AVS than those in other parts of the U.S. , and on a larger scale, a national study reports that domain-specific attitudes are more indicative of the acceptance of driverless vehicles than sociodemographic characteristics (Nordhoff et al. 2018). Safety and security concerns regarding the risks of AV technology can also negatively affect the adoption of selfdriving vehicles (Benleulmi and Blecker 2017;Nazari et al., 2019). ...
Exploring Motivating Factors and Constraints of Using and Adoption of Shared Autonomous Vehicles (SAVs)
Article
Full-text available
  • Mar 2023
Self-driving vehicles are expected to reduce mobility barriers; however, it is still unclear how individuals will use them and how they will benefit the urban transportation system overall. This research aims to evaluate self-driving technology diffusion by applying and testing a conceptual model that was designed to unpack the possible determinants of the adoption of shared autonomous vehicles (SAVs). The study framework was developed based on the principles of socio-psychological theories of human behavior and investigates the adoption of SAVs by two groups of people: users and non-users. Structural equation modeling (SEM) was utilized to analyze the effects of motivational and restriction-related factors on SAV use and adoption, and the results indicated that perceived usefulness and restriction-related factors can positively motivate individuals to use SAVs more frequently. Ultimately, however, their adoption will depend on the public’ attitudes towards technology and as their perceptions of the inherent risks. This study provides new insights into the identification of potential SAV users and non-users and shows how their behavioral intentions differ.
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... SAVs have received much attention recently as a new technology that can deal with equity and efficiency challenges in transportation. Accordingly, multiple studies have explored AV technology (25,26) and the adoption of SAVs (4,11,27,28) through use of stated preference surveys, focus group discussions, and simulation methods. A rich body of literature has explored AVs and contributed to the adoption of AVs and SAVs. ...
... They found that only 75% of individuals would choose SAVs even if the services were provided free of charge, and 44% of individuals were inclined to use regular vehicles instead of AVs. Nazari et al. explored travelers' safety concerns about AV technology through a recursive bivariate ordered probit model using a data set provided by the California Energy Commission (25). The results indicated that AV safety concerns could negatively influence adoption of AVs. ...
Modeling Users’ Adoption of Shared Autonomous Vehicles Employing Actual Ridership Experiences
Article
Full-text available
  • Mar 2022
Despite the growing interest in implementing shared autonomous vehicles (SAVs) as a new mobility mode, there is still a lack of methodologies to unpack SAV adoption by individuals after experiencing self-driving vehicles. This study aimed to fill this gap by analyzing data collected from a users’ survey of a self-driving shuttle piloted downtown and on a university campus in Arlington, TX. Employing structural equation modeling, the hypothesized relationships between SAV adoption and key factors were tested. Data analyses indicated that individuals with limited access to a private vehicles, low-income people, young adults, university students, males, and Asians were more likely to ride this new service. Furthermore, results showed that SAV service attributes, including internal and external service performance and usual transportation mode, affected users’ willingness to continue using the service in the future. The study also highlighted the role of trip waiting time, -purpose, and -frequency on SAV adoption. Our model simultaneously considered usual transportation mode and trip frequency as factors that could mediate the role of vehicle ownership on SAV adoption. The results suggested that participants with greater access to a private vehicle were strongly interested in using private vehicles and less likely to use the ridesharing alternative, consequently they less frequently used the piloted SAV. The outcomes from this study are expected to inform planners with advanced knowledge about emerging technology to help them to adjust SAV policies before autonomous vehicle services are fully on the roads.
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... Given the importance of using a new mobility service, several studies have analyzed the factors behind the SAV adoption while focusing on perceived benefits and perceived ease of use of AVs, preferences, travelers' safety concerns, commuting behaviors, and attitudes toward using SAVs (23)(24)(25)(26)(27). ...
Capturing the Behavioral Determinant behind the Use and Adoption of Shared Autonomous Vehicles (SAVs)
Conference Paper
Full-text available
  • Oct 2022
Self-driving vehicles have the potential to reduce mobility barriers by providing affordable and flexibleshared mobility options. However, integrating the benefits of self-driving vehicles into the current transitsystem depends highly on public acceptance and use of this new mobility mode when it is extensivelyavailable on the road. This paper seeks to advance self-driving technology diffusion by applying and testinga conceptual model designed to explore the possible determinants of using and adopting shared autonomousvehicles (SAVs). Accordingly, we utilized principles of socio-psychological theories of human behavior todevelop the study framework and investigate two groups of people; a sample of people who used anavailable SAV on the road and a sample of non-users. We tested the validity of the research hypothesis byusing the structural equation model (SEM) and examined the effects of motivations and restriction-relatedfactors on SAV use and adoption. Results reveal that two behavioral factors of perceived usefulness andrelatedness can increase respondents' motivations to use the available SAV more frequently. However, thefuture adoption of SAVs by both users and non-users is highly associated with individuals’ attitudes towardsthis technology. We also found that the perception of SAV risks can impede non-users from adopting theservice in the future as well. The results of this study imply that the effective implementation of SAVs callsfor a deep understanding of the behavioral motivations people experience while encountering mobilityinnovations.
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... Haboucha et al. (2017) found that early AV adopters will likely be young, students, more educated, and spend more time in vehicles. Nazari et al. (2019a) tested the hypothesis that AV adoption is controlled by, among various factors, the safety concern of travelers, which is itself a function of exogenous factors. The framework simultaneously modeled AV adoption and safety concerns while considering the endogeneity between the two dependent variables. ...
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Millennials and automated mobility: exploring the role of generation and attitudes on AV adoption and willingness-to-pay
Article
Full-text available
  • Aug 2022
Results from a recent travel behavior survey are used to examine the impacts of generational and attitudinal factors on autonomous vehicle (AV) adoption and willingness to pay (WTP). Initial exploratory data analysis confirmed generational differences in various aspects of travel behavior. A structural equation model (SEM) was developed to capture the causal effects of different variables on AV adoption/WTP. In particular, Interaction effects with generational cohorts were analyzed. Generally, it could be inferred that WTP significantly and positively affects adoption levels, so do technology savviness, the desire for driving assistance/higher safety features, and mobility for non-drivers. The WTP is also affected by income, employment status, and previous technology experience. Further, our model suggests that millennials who appreciate the on-demand aspect of AVs are more likely to adopt a fully or partially automated vehicle. The middle-aged cohort (35–39 years old) as well as transit-user millennials are likely to have higher WTP values.
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Literature review on surveys investigating the acceptance of automated vehicles
Article
Full-text available
  • Nov 2017
  • TRANSPORTATION
Due to the potential of automated vehicles to offer a multitude of advantages to the travelers and therefore influence their daily routines, it is essential to monitor the public’s opinion on this particular technological development. The goal of a number of surveys in recent years was therefore not only to elicit the general acceptance of the technology but to additionally explore when, how and why respondents were inclined to make use of it. This is the first literature review on surveys regarding automated vehicles with the intention to investigate the various methods currently being applied and the conclusions they lead to. In addition to comparing the general results in terms of the distributions of the response variables, the surveyed explanatory variables are categorized and analyzed according to their influence in different experiments. Based on these investigations, this review identifies research gaps that can be addressed in future experiments.
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Assessing public opinions of and interest in new vehicle technologies: An Austin perspective
Article
Full-text available
  • Jun 2016
  • TRANSPORT RES C-EMER
Technological advances are bringing connected and autonomous vehicles (CAVs) to the ever-evolving transportation system. Anticipating public acceptance and adoption of these technologies is important. A recent internet-based survey polled 347 Austinites to understand their opinions on smart-car technologies and strategies. Results indicate that respondents perceive fewer crashes to be the primary benefit of autonomous vehicles (AVs), with equipment failure being their top concern. Their average willingness to pay (WTP) for adding full (Level 4) automation ($7253) appears to be much higher than that for adding partial (Level 3) automation ($3300) to their current vehicles. Ordered probit and other model specifications estimate the impact of demographics, built-environment variables, and travel characteristics on Austinites’ WTP for adding various automation technologies and connectivity to their current and coming vehicles. It also estimates adoption rates of shared autonomous vehicles (SAVs) under different pricing scenarios ($1, $2, and $3 per mile), choice dependence on friends’ and neighbors’ adoption rates, and home-location decisions after AVs and SAVs become a common mode of transport. Higher-income, technology-savvy males, who live in urban areas, and those who have experienced more crashes have a greater interest in and higher WTP for the new technologies, with less dependence on others’ adoption rates. Such behavioral models are useful to simulate long-term adoption of CAV technologies under different vehicle pricing and demographic scenarios. These results can be used to develop smarter transportation systems for more efficient and sustainable travel.
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Preparing a nation for autonomous vehicles: Opportunities, barriers and policy recommendations
Article
Full-text available
  • Jul 2015
  • TRANSPORT RES A-POL
Autonomous vehicles (AVs) represent a potentially disruptive yet beneficial change to our transportation system. This new technology has the potential to impact vehicle safety, congestion, and travel behavior. All told, major social AV impacts in the form of crash savings, travel time reduction, fuel efficiency and parking benefits are estimated to approach $2000 to per year per AV, and may eventually approach nearly $4000 when comprehensive crash costs are accounted for. Yet barriers to implementation and mass-market penetration remain. Initial costs will likely be unaffordable. Licensing and testing standards in the U.S. are being developed at the state level, rather than nationally, which may lead to inconsistencies across states. Liability details remain undefined, security concerns linger, and without new privacy standards, a default lack of privacy for personal travel may become the norm. The impacts and interactions with other components of the transportation system, as well as implementation details, remain uncertain. To address these concerns, the federal government should expand research in these areas and create a nationally recognized licensing framework for AVs, determining appropriate standards for liability, security, and data privacy.
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Intention to use a fully automated car: Attitudes and a priori acceptability
Article
Full-text available
  • Nov 2014
If previous research studied acceptability of partially or highly automated driving, few of them focused on fully automated driving (FAD), including the ability to master longitudinal control, lateral control and maneuvers. The present study analyzes a priori acceptability, attitudes, personality traits and intention to use a fully automated vehicle. 421 French drivers (153 males, M = 40.2 years, age range 19–73) answered an online questionnaire. 68.1% Of the sample a priori accepted FAD. Predictors of intention to use a fully automated car (R2 = .671) were mainly attitudes, contextual acceptability and interest in impaired driving (i.e. the two components of FAD acceptability), followed by driving related sensation seeking, finally gender. FAD preferred use cases were on highways, in traffic congestion and for automatic parking. Furthermore, some drivers reported interest in impaired driving misuses, despite awareness of their responsibility for both the vehicle and the driving. These results are discussed regarding previous knowledge about acceptability of advanced driving assistance systems and consequences for the use of fully automated cars.
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Modeling Ordered Choices: A Primer
Book
Full-text available
  • Jan 2009
It is increasingly common for analysts to seek out the opinions of individuals and organizations using attitudinal scales such as degree of satisfaction or importance attached to an issue. Examples include levels of obesity, seriousness of a health condition, attitudes towards service levels, opinions on products, voting intentions, and the degree of clarity of contracts. Ordered choice models provide a relevant methodology for capturing the sources of influence that explain the choice made amongst a set of ordered alternatives. The methods have evolved to a level of sophistication that can allow for heterogeneity in the threshold parameters, in the explanatory variables (through random parameters), and in the decomposition of the residual variance. This book brings together contributions in ordered choice modeling from a number of disciplines, synthesizing developments over the last fifty years, and suggests useful extensions to account for the wide range of sources of influence on choice.
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Driving to safety: How many miles of driving would it take to demonstrate autonomous vehicle reliability?
Article
  • Dec 2016
  • TRANSPORT RES A-POL
How safe are autonomous vehicles? The answer is critical for determining how autonomous vehicles may shape motor vehicle safety and public health, and for developing sound policies to govern their deployment. One proposed way to assess safety is to test drive autonomous vehicles in real traffic, observe their performance, and make statistical comparisons to human driver performance. This approach is logical, but it is practical? In this paper, we calculate the number of miles of driving that would be needed to provide clear statistical evidence of autonomous vehicle safety. Given that current traffic fatalities and injuries are rare events compared to vehicle miles traveled, we show that fully autonomous vehicles would have to be driven hundreds of millions of miles and sometimes hundreds of billions of miles to demonstrate their reliability in terms of fatalities and injuries. Under even aggressive testing assumptions, existing fleets would take tens and sometimes hundreds of years to drive these miles?an impossible proposition if the aim is to demonstrate their performance prior to releasing them on the roads for consumer use. These findings demonstrate that developers of this technology and third-party testers cannot simply drive their way to safety. Instead, they will need to develop innovative methods of demonstrating safety and reliability. And yet, the possibility remains that it will not be possible to establish with certainty the safety of autonomous vehicles. Uncertainty will remain. Therefore, it is imperative that autonomous vehicle regulations are adaptive?designed from the outset to evolve with the technology so that society can better harness the benefits and manage the risks of these rapidly evolving and potentially transformative technologies.
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Cost and benefit estimates of partially-automated vehicle collision avoidance technologies
Article
  • Jul 2016
Many light-duty vehicle crashes occur due to human error and distracted driving. Partially-automated crash avoidance features offer the potential to reduce the frequency and severity of vehicle crashes that occur due to distracted driving and/or human error by assisting in maintaining control of the vehicle or issuing alerts if a potentially dangerous situation is detected. This paper evaluates the benefits and costs of fleet-wide deployment of blind spot monitoring, lane departure warning, and forward collision warning crash avoidance systems within the US light-duty vehicle fleet. The three crash avoidance technologies could collectively prevent or reduce the severity of as many as 1.3 million U.S. crashes a year including 133,000 injury crashes and 10,100 fatal crashes. For this paper we made two estimates of potential benefits in the United States: (1) the upper bound fleet-wide technology diffusion benefits by assuming all relevant crashes are avoided and (2) the lower bound fleet-wide benefits of the three technologies based on observed insurance data. The latter represents a lower bound as technology is improved over time and cost reduced with scale economies and technology improvement. All three technologies could collectively provide a lower bound annual benefit of about $18 billion if equipped on all light-duty vehicles. With 2015 pricing of safety options, the total annual costs to equip all light-duty vehicles with the three technologies would be about $13 billion, resulting in an annual net benefit of about $4 billion or a $20 per vehicle net benefit. By assuming all relevant crashes are avoided, the total upper bound annual net benefit from all three technologies combined is about $202 billion or an $861 per vehicle net benefit, at current technology costs. The technologies we are exploring in this paper represent an early form of vehicle automation and a positive net benefit suggests the fleet-wide adoption of these technologies would be beneficial from an economic and social perspective.
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Gender Economics Courses in Liberal Arts Colleges
Article
  • Sep 1997
Gender has become a “hot” research topic in recent years and has begun making its way into the classroom (Conrad 1992). Interest in gender issues has spread, but only a small proportion of economics departments beyond the few top national liberal arts colleges include courses in gender economics.
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Statistical and Econometric Methods for Transportation Data Analysis
Article
  • Apr 2003
Preface Introduction Transportation is integral to developed societies. It is responsible for personal mobility which includes access to services, goods, and leisure. It is also a key element in the delivery of consumer goods. Regional, state, national, and the world economy rely upon the efficient and safe functioning of transportation facilities. In addition to the sweeping influence transportation has on economic and social aspects of modern society, transportation issues pose challenges to professionals across a wide range of disciplines including transportation engineers, urban and regional planners, economists, logisticians, systems and safety engineers, social scientists, law enforcement and security professionals, and consumer theorists. Where to place and expand transportation infrastructure, how to safely and efficiently operate and maintain infrastructure, and how to spend valuable resources to improve mobility, access to goods, services and healthcare, are among the decisions made routinely by transportation-related professionals. Many transportation-related problems and challenges involve stochastic processes that are influenced by observed and unobserved factors in unknown ways. The stochastic nature of these problems is largely a result of the role that people play in transportation. Transportation-system users are routinely faced with decisions in contexts such as what transportation mode to use, which vehicle to purchase, whether or not to participate in a vanpool or telecommute, where to relocate a business, whether or not to support a proposed light-rail project and whether to utilize traveler information before or during a trip. These decisions involve various degrees of uncertainty. Transportation-system managers and governmental agencies face similar stochastic problems in determining how to measure and compare system measures of performance, where to invest in safety improvements, how to efficiently operate transportation systems and how to estimate transportation demand. As a result of the complexity, diversity, and stochastic nature of transportation problems, the methodological toolbox required of the transportation analyst must be broad. Approach The third edition of Statistical and Econometric Methods offers an expansion over the first and second editions in response to the recent methodological advancements in the fields of econometrics and statistics, to address reader and reviewer comments on the first and second editions, and to provide an increasing range of examples and corresponding data sets. This book describes and illustrates some of the statistical and econometric tools commonly used in transportation data analysis. Every book must strike an appropriate balance between depth and breadth of theory and applications, given the intended audience. This book targets two general audiences. First, it can serve as a textbook for advanced undergraduate, Masters, and Ph.D. students in transportation-related disciplines including engineering, economics, urban and regional planning, and sociology. There is sufficient material to cover two 3-unit semester courses in statistical and econometric methods. Alternatively, a one semester course could consist of a subset of topics covered in this book. The publisher’s web-site contains the numerous datasets used to develop the examples in this book so that readers can use them to reinforce the modeling techniques discussed throughout the text. The book also serves as a technical reference for researchers and practitioners wishing to examine and understand a broad range of statistical and econometric tools required to study transportation problems. It provides a wide breadth of examples and case studies, covering applications in various aspects of transportation planning, engineering, safety, and economics. Sufficient analytical rigor is provided in each chapter so that fundamental concepts and principles are clear and numerous references are provided for those seeking additional technical details and applications. Data-Driven Methods vs. Statistical and Econometric Methods In the analysis of transportation data, four general methodological approaches have become widely applied: data-driven methods, traditional statistical methods, heterogeneity models, and causal inference models (the latter three of which fall into the category of statistical and econometric methods and are covered in this text). Each of these methods have an implicit trade-off between practical prediction accuracy and their ability to uncover underlying causality. Data-driven methods include a wide range of techniques including those relating to data mining, artificial intelligence, machine learning, neural networks, support vector machines, and others. Such methods have the potential to handle extremely large amounts of data and provide a high level of prediction accuracy. On the down side, such methods may not necessarily provide insights into underlying causality (truly understanding the effects of specific factors on accident likelihoods and their resulting injury probabilities). Traditional statistical methods provide reasonable predictive capability and some insight into causality, but they are eclipsed in both prediction and providing causal insights by other approaches Heterogeneity models extend traditional statistical and econometric methods to account for potential unobserved heterogeneity (unobserved factors that may be influencing the process of interest). Causal-inference models use statistical and econometric methods to focus on underlying causality, often sacrificing predictive capability to do so. Even though data-driven methods are often a viable alternative to the analysis of transportation data if one is interested solely in prediction and not interested in uncovering causal effects, because the focus of this book is uncovering issues of causality using statistical and econometric methods, data-driven methods are not covered. Chapter topics and organization Part I of the book provides statistical fundamentals (Chapters 1 and 2). This portion of the book is useful for refreshing fundamentals and sufficiently preparing students for the following sections. This portion of the book is targeted for students who have taken a basic statistics course but have since forgotten many of the fundamentals and need a review. Part II of the book presents continuous dependent variable models. The chapter on linear regression (Chapter 3) devotes additional pages to introduce common modeling practice—examining residuals, creating indicator variables, and building statistical models—and thus serves as a logical starting chapter for readers new to statistical modeling. The subsection on Tobit and censored regressions is new to the second edition. Chapter 4 discusses the impacts of failing to meet linear regression assumptions and presents corresponding solutions. Chapter 5 deals with simultaneous equation models and presents modeling methods appropriate when studying two or more interrelated dependent variables. Chapter 6 presents methods for analyzing panel data—data obtained from repeated observations on sampling units over time, such as household surveys conducted several times to a sample of households. When data are collected continuously over time, such as hourly, daily, weekly, or yearly, time series methods and models are often needed and are discussed in Chapters 7 and 8. New to the 2nd edition is explicit treatment of frequency domain time series analysis including Fourier and Wavelets analysis methods. Latent variable models, discussed in Chapter 9, are used when the dependent variable is not directly observable and is approximated with one or more surrogate variables. The final chapter in this section, Chapter 10, presents duration models, which are used to model time-until-event data as survival, hazard, and decay processes. Part III in the book presents count and discrete dependent variable models. Count models (Chapter 11) arise when the data of interest are non-negative integers. Examples of such data include vehicles in a queue and the number of vehicle crashes per unit time. Zero inflation—a phenomenon observed frequently with count data—is discussed in detail and a new example and corresponding data set have been added in this 2nd edition. Logistic Regression is commonly used to model probabilities of binary outcomes, is presented in Chapter 12, and is unique to the 2nd edition. Discrete outcome models are extremely useful in many study applications, and are described in detail in Chapter 13. A unique feature of the book is that discrete outcome models are first considered statistically, and then later related to economic theories of consumer choice. Ordered probability models (a new chapter for the second edition) are presented in Chapter 14. Discrete-continuous models are presented in Chapter 15 and demonstrate that interrelated discrete and continuous data need to be modeled as a system rather than individually, such as the choice of which vehicle to drive and how far it will be driven. Finally, Part IV of the book contains massively expanded chapter on random parameters models (Chapter 16), a new chapter on latent class models (Chapter 17), a new chapter on bivariate and multivariate dependent variable models (Chapter 18) and an expanded chapter on Bayesian statistical modeling (Chapter 19). Models that deal with unobserved heterogeneity (random parameters models and latent class models) have become the standard statistical approach in many transportation sub-disciplines and Chapters 16 and 17 provide an important introduction to these methods. Bivariate and multivariate dependent variable models are encountered in many transportation data analyses. Although the inter-relation among dependent variables has often been ignored in transportation research, the methodologies presented in Chapter 18 show how such inter-dependencies can be accurately modeled. The chapter on Bayesian statistical models (Chapter 19) arises as a result of the increasing prevalence of Bayesian inference and Markov Chain Monte Carlo Methods (an analytically convenient method for estimating complex Bayes’ models). This chapter presents the basic theory of Bayesian models, of Markov Chain Monte Carlo methods of sampling, and presents two separate examples of Bayes’ models. The appendices are complementary to the remainder of the book. Appendix A presents fundamental concepts in statistics which support analytical methods discussed. Appendix B provides tables of probability distributions used in the book, while Appendix C describes typical uses of data transformations common to many statistical methods. While the book covers a wide variety of analytical tools for improving the quality of research, it does not attempt to teach all elements of the research process. Specifically, the development and selection of research hypotheses, alternative experimental design methodologies, the virtues and drawbacks of experimental versus observational studies, and issues involved with the collection of data are not discussed. These issues are critical elements in the conduct of research, and can drastically impact the overall results and quality of the research endeavor. It is considered a prerequisite that readers of this book are educated and informed on these critical research elements in order to appropriately apply the analytical tools presented herein. Simon P. Washnington Mathew G. Karlaftis Fred L. Mannering Panigiotis Ch. Anastasopoulos
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