ArticlePDF Available

Trust More, Fear Less: The Role of Social Support in Fully Automated Vehicle Choice

September 2022
IEEE Transactions on Engineering Management

September 2022

DOI:10.1109/TEM.2022.3208567

Authors:

Kyushu University

We reveal how mental support from family, friends, and local authorities-social support (SP)-contributes to inclusivity in the context of fully autonomous vehicles (FAVs). We focus on disaster victims who have trouble driving and test how the information on traumatic experiences relates to their purchase decisions. To this end, we create a unique survey that asks about respondents' purchasing decisions before/after we share information with them on FAV traffic accidents that led to a driver's death. Our results show that SP is crucial for encouraging people to choose FAVs, even after fatal news concerning FAVs is shared, regardless of disaster experience. We also find that other factors, such as safety and convenience, can encourage people to 'substitute' choosing FAVs in place of not choosing FAVs. By adopting integrated choice and latent variable (ICLV) models considering individual heterogeneity over 60,000 respondents, our results indicate possible policy paths through which to utilize SP in creating FAVs as an inclusive transportation mode.

Content uploaded by Sunbin Yoo

Content may be subject to copyright.

Trust more, Fear less:

The Role of Social Support in Fully Automated Vehicle Choice

Sunbin Yoo, Junya Kumagai and Shunsuke Managi

We reveal how mental support from family,

friends, and local authorities—social support (SP)—

contributes to inclusivity in the context of fully

autonomous vehicles (FAVs). We focus on disaster

victims who have trouble driving and test how the

information on traumatic experiences relates to their

purchase decisions. To this end, we create a unique

survey that asks about respondents’ purchasing

decisions before/after we share information with

them on FAV traﬃc accidents that led to a driver’s

death. Our results show that SP is crucial for

encouraging people to choose FAVs, even after

fatal news concerning FAVs is shared, regardless of

disaster experience. We also ﬁnd that other factors,

such as safety and convenience, can encourage

people to ‘substitute’ choosing FAVs in place of

not choosing FAVs. By adopting integrated choice

and latent variable (ICLV) models considering

individual heterogeneity over 60,000 respondents,

our results indicate possible policy paths through

which to utilize SP in creating FAVs as an inclusive

transportation mode.

JEL Classiﬁcation: L62, Q48, Q55, Q58.

Keywords: Autonomous vehicle; Self-driving car;

Emerging transport modes; Mobility on-demand; De-

mand estimation; Integrated choice and latent vari-

able approach

Urban Institute & School of Engineering, Kyushu University,

yoo.sunbin.277@m.kyushu-u.ac.jp

I. I

We explore whether mental support encour-

ages people with traumatic experiences to choose

new technology, taking fully autonomous vehicles

(FAVs) as an example. Previous works have found

that FAVs can revolutionize the transportation

paradigm by reducing vehicle emissions ((1), (2)),

increasing road and travel eﬃciency ((3), (4), (5),

(6)), and substantially reducing the number of

traﬃc accidents due to human error ((7), (8), (9),

and (10)).Inspired by previous works ((11) and

(12)), we start from the viewpoint that these ben-

eﬁts will be maximized if FAVs become inclusive

and embrace socially excluded vulnerable people.

We contribute to the literature on the impact of

social support (SP) on new technology adoption.

The previous works have already shown that SP

from family, friends, local authorities, and volun-

teers is critical in encouraging people to adapt to

new technology((13);(14)). In this study, we extend

the ﬁndings of these works to those who urgently

require SP—those who cannot acquire mobil-

ity due to physical/cognitive/mental challenges1.

Among the various types of shocks, we choose to

1In this study, while there are various types of ‘support’, such

as ﬁnancial support, we focus on mental support that incor-

porates encouragement, trustful relationships and reliability.

consider individuals who are exposed to natural

disasters (NDs)—disaster victims—because disas-

ter experiences may induce the loss/changes of

mental, cognitive, and physical capability, which

may motivate such individuals to choose FAVs.

In this regard, taking Japan as our study area has

several advantages. Japan is particularly vulnera-

ble to NDs because of its climate and topography,

as evidenced by its experiences with countless

earthquakes, typhoons, and other types of disas-

ter. Our data provide a sample that includes over

10,000 disaster victim observations. This setting

allows our results to be more realistic because for

an individual to lose the ability to drive due to a

disaster, it may require multiple ND experiences,

which are likely to occur in Japan. Thus, our set-

ting eliminates the necessity of soliciting the time

and name of a speciﬁc disaster that caused phys-

ical and mental damage to respondents. While

we focus on disaster victims and FAVs, as our

study concerns how people with risk experiences

respond to new technologies, the implications

of our results can be extended beyond disaster

victims and FAVs.

We design and conduct a set of original surveys

that contain information on a fatal FAV accident,

which is likely to shift people’s preferences away

from such vehicles. We include a famous exam-

ple in our survey: the Tesla autopilot crushing

incident that resulted in a driver’s death, which

occurred in Delray Beach, Florida, in March 2016.

The incident raised concerns about Tesla’s autopi-

lot technology shortly after the National Trans-

portation Safety Board stated that the company’s

autopilot driver assistance system had been en-

gaged during that fatal crash. We share the acci-

dent information with the survey respondents and

ask them again about their preferences.

Taking advantage of the data of over 60,000

respondents from 2015 to 2017 in Japan, we ask

the respondents twice about their willingness to

buy (WTB; purchasing decision) FAVs: before/after

we share the accident information with them.

Simply, the survey is set up as follows:

Step 1. The respondents are asked to write down

whether they wish to purchase FAVs.

Step 2. We share information on the Tesla autopilot

accident, which resulted in the death of the

driver.

Step 3. The respondents are again asked to write

down whether they wish to purchase FAVs.

Steps 1 and 3 are written separately.

We include the following items that reﬂect the

respondents’ psychological intentions to adopt

FAVs—risk perceptions (RPs), traﬃc accidents,

convenience due to FAVs, SP, and environmen-

tal concerns. We identify these items, whenever

possible, based on statements that have previ-

ously been used and found to be eﬀective in the

literature. Methodologically, we are interested in

capturing the utility associated with FAV choices

regarding the above factors. The goal is to esti-

mate a utility function (random utility function)

to model FAV choices. We additionally investigate

whether SP plays a pivotal role in substituting for

those individuals who are reluctant to choose FAVs

before the accident news is shared.

We consider two aspects: 1) the utility function

of socially excluded people may diﬀer from those

who are not socially excluded, and 2) individ-

ual heterogeneity within/without a group may

exist. To this end, we choose integrated choice

and latent variable (ICLV) models, which have

recently become an increasingly popular exten-

sion of discrete choice models that attempt to

explicitly model the cognitive process underlying

the formation of any choice ((15)). We model our

utility functions to allow for one or more of the pa-

rameters in the model to be randomly distributed,

thus allowing for the coeﬃcients in the model to

vary across decision makers. Figure 1shows our

study structure. We ﬁrst estimate demand using

the answers from the 1st purchase decision, share

the accident news and then estimate the factors

closely correlated with the ﬁnal decision.

The remainder of this paper is structured as fol-

lows. Section II provides background information

on the industry and policy. The data and model

are presented in Sections III and IV, respectively.

Section ?? reports the empirical results. Section

VI-B addresses our ﬁndings and provides policy

implications. Section VII concludes the paper.

II. B

This section ﬁrst deﬁnes FAVs and then reviews

previous works on FAVs focusing on their potential

beneﬁts, concerns, and demand patterns. Then,

we discuss the related literature, identify the gap

in the literature and present our contributions.

Then, we discuss why our study addresses FAVs in

achieving inclusive transport by focusing on social

exclusion, traﬃc accidents and ND experiences.

A. FAVs

FAVs have diﬀerent levels of automatic opera-

tion, as presented in Figure 2. A system generally

unconditionally performs all driving operations at

level 5 (fully automated); however, as of 2021, a

fully automated system has not yet been realized

in Japan, and the Japanese government aims to

achieve this goal by 2025. Currently, consumers

cannot select the speciﬁc technology or devices

to integrate into their new cars when purchas-

ing level 1 and 2 AVs.2For high and full lev-

els of automation (levels 4 and 5, respectively),

cars are expected to pass driving qualiﬁcation

tests that inspect whether obligatory automation-

related equipment and accessories are fully oper-

ational. As illustrated in Figure 2, the Society of

Automotive Engineers (SAE) has deﬁned diﬀerent

levels of automated functionality, ranging from no

automated features (level 0) to full automation

(level 5, commonly referred to as autonomous,

self-driving or driverless vehicles).

B. FAVs and Social Exclusion

FAVs can create mobility opportunities by pro-

viding access to socially excluded people who are

unable to drive, such as elderly individuals (with

noticeable problems in terms of driving capaci-

ties) and physically/mentally disabled people ((16)

and (17)). However, despite the soaring interest in

FAVs among researchers, whether FAVs can em-

brace socially excluded people remains relatively

unclear.

Recognizing the potential of FAVs, on March

29, 2019, the Japanese cabinet announced that

FAVs should pursue a transition to become more

ﬂexible to change and respond as FAV technology

evolves, thereby allowing for the full realization

2We refer to 2021.

Fig. 1: Study Structure

of FAV beneﬁts. This transition aimed not only

to improve eﬃciency based on existing socially

accepted objectives (e.g., improvement of conve-

nience and prevention of accidents) but also the

realization of new value created by the diversiﬁ-

cation of the objectives themselves (by including

socially vulnerable people in the policy target)

and the resolution of problems such as transport

disparity, social exclusion, and transportation dis-

advantage.3

The Japanese government has emphasized the

necessity of building a transportation system ca-

pable of resolving social problems by listening to

the opinions of various stakeholders, including

those of stakeholders for whom it is challenging

to speak up in social situations. The Japanese gov-

ernment is urging the implementation of ﬂexible

3We refer to the national report published by the Japanese

Government Cabinet Secretariat, "Social Principles of Human-

Centric AI," accessed August 17, 2021.

and eﬀective legislative systems that can include

socially vulnerable people by inspiring the eﬀorts

of automotive manufacturers to develop technolo-

gies that can assist these people.

C. Literature Review and Our Contributions

Our study provides a pathway through which to

create inclusive FAVs. Previous works have shown

how FAVs could be appreciated/adopted by the

general public ((18)) and have usually concluded

that younger, richer and better educated individ-

uals are more likely to choose FAVs ((19), (20),

and (21)). Moreover, we ﬁnd limited numbers of

previous works that have dealt with FAV demand

in the social exclusion context. A notable excep-

tion is (22), which focused on the role of AVs for

elderly people in overcoming hindrances to travel

because older adults are likely to experience trans-

port disadvantages, transport poverty, and the risk

of social exclusion. Inspired by (22), we answer the

Fig. 2: Explanations of AV Technology by Level.

Source: SAE International (2021, Accessed August 4, 2021)

following questions. What is the likely outcome

for individuals experiencing physical or cognitive

challenges? People faced with cognitive challenges

may ﬁnd it diﬃcult to operate new technologies

and to override controls ((23)). Despite the impor-

tance of answering this question in creating inclu-

sive FAVs, to the best of our knowledge, no pre-

vious works have answered it. Are factors closely

correlated with public acceptance of FAVs still

crucial for the acceptance/appreciation of FAVs

among risk-experienced people? What are the dif-

ferences between risk-experienced people and the

general public? The answers to these questions

can be extended to how risk-experienced people

respond to new technologies.

D. ND Victims

We focus on disaster victims because NDs are

massive shocks that substantially reshape individ-

uals’ RPs, which may prevent them from driv-

ing (and therefore cause them to prefer FAVs)

or adopting new technology. Previous works have

usually shown that risk-averse individuals are un-

likely to adopt FAVs ((24) and (25)), which stems

from the ﬁndings of previous works that risk-

averse individuals do not prefer to adopt new

technologies ((26);(27);(28)). Understanding the

diﬀerent realms of risk perspective illuminates the

path through which to provide FAVs as inclusive

transport modes by suggesting how to include

people with/without diverse types of shock, who

thus drive the change in risk preference. These

diﬀerences imply that the factors motivating those

who have experienced shocks require diﬀerent

solutions. To address this issue, we include a latent

variable that reﬂects people’s risk perspectives and

shows whether they prefer higher risks.

Thus, we proceed by expecting ND experiences

to increase RPs ((29), (30), (31), (32)) and that

increased RPs discourage victims from accepting

new technologies ((33), (34)). Such trends can also

be found in the ﬁeld of transportation (which

is usually represented by a new mode choice or

driving behavior) ((35), (36), (37)). Among these

studies, previous works on FAVs have reported

that increased risk hinders the use and adoption

of FAVs ((38), (39), (40)). Regarding travel behavior,

disaster victims who have experienced trauma are

likely to lose their driving ability due to the fear

of road accidents; some studies have found that if

such individuals drive, then their traﬃc behaviors

will be positively correlated with their likelihood

of having automobile accidents ((41), (42)).

III. D

In this section, we present and describe the

survey, present the data, and explain our empirical

model referring to the previous literature.

A. Survey Method

We ﬁrst conduct three online surveys in Japan

in 2015, 2016, and 2017. Surveys are administered

to individuals aged 18 years and older. We ran-

domly select the respondents while maintaining

their gender and age distributions in a manner

similar to those of the Japanese population.4Con-

sequently, we have 69,647 respondents. To assess

the sample representativeness of the survey, we

present the distribution of socioeconomic vari-

ables of the survey and Japanese census data

in Appendix Table A1. We ﬁnd that there are

slight diﬀerences in gender and education levels

between our survey respondents and Japanese

census data. Before starting the large-scale sur-

vey, a presurvey is administered to calibrate the

questionnaires.

In what follows, we explain our data by dividing

the explanation into the areas of 1) survey struc-

ture, 2) FAV-related questions, 3) identifying those

who have experienced risks, 4) socioeconomic

variables and 5) latent variables.

B. Survey Structure

As mentioned in Section I, we ﬁrst ask the

respondents about their WTB on FAVs (Step 1).

Then, we share the information on the Tesla au-

topilot accident that resulted in the driver’s death

(Step 2). Next, the respondents are again required

to write down their WTB (Step 3). Steps 1 and 3

are written separately. Figure 3shows the survey

items and answers. We present the survey items to

the respondents in the order of the item numbers.

4This process is possible because we employ an internet

survey conducted by Nikkei Research Inc., the largest research

company in Japan. Several trap questions are included in the

survey to identify respondents who have not seriously an-

swered the questions. Those who have not correctly answered

such trap questions are excluded from the survey company’s

sample in the collection process.

Next, we elicit the perceived FAV value (here-

after, FAV value) for FAVs, which refers to the per-

ceived value of FAVs expressed in monetary terms.

Studies in the ﬁeld of transportation have con-

cluded that perceived value works as a predictor of

purchase intention. ((43);(44)), and (45) and (46)

suggested the role of perceived value in terms of

autonomous vehicles and shuttles. Such analyses

are recommended because perceived value indi-

cates an inﬂuence on behavioral intentions, which

this study aims to consider by constructing latent

variables. Thus, in this study, FAV values work as

an index of appreciation for FAVs rather than the

costs of automation, which exhibit a negative re-

lationship with purchase intention. Therefore, it is

quite natural that a higher FAV value is positively

correlated with a higher chance of purchasing

FAVs.

For question number Q3-1, on accident aware-

ness, we do not sort our respondents according to

their answers; rather, we use accident awareness

as a dummy variable equal to 1 if a respondent

is aware of the accident and 0 otherwise. Because

we are interested in how people’s choices change

according to the accident information shared with

them, we take Question Q3-2 as the ﬁnal WTB

decision and take the diﬀerences between Q3-2

and Q3-3 as one of our main dependent variables.

C. FAV-related questions

For the questions related to FAV purchase in-

tention, the respondents are asked the following

question: “Do you want to add a completely self-

driving option that allows you to move around

when you purchase a car in the future?" The re-

spondents are then oﬀered the following response

options: “(1) Purchase for sure, (2) Purchase under

certain conditions, (3) Do not purchase, and (4) I

don’t know". Given that FAVs had not yet been

fully introduced into the market in 2017, we as-

sume that people who show an aﬃnity for FAVs

can be potential future consumers.5Therefore, we

include those who respond (1) or (2) in a group of

‘potential consumers’ because they show aﬃnity

for FAV use. Moreover, people who respond (3)

or (4) are reluctant to purchase FAVs, and thus,

we do not consider them potential consumers.

Therefore, we code WTB as equal to 1 if a respon-

dent belongs to the potential consumer group and

0 otherwise. Therefore, our analysis allows us to

identify which types of factors shift consumers

who belong to (3) or (4) to (1) or (2).

Note that we make a clear distinction be-

tween “adding” a completely self-driving option

and “purchasing” an FAV by asking the following:

“When you purchase a car in the future, do you

want to add a completely self-driving option that

allows you to move around?” Such a setting al-

lows us to clearly distinguish between ‘purchasing

a car’ and ‘adding a self-driving option’, which

allows us to exclude potential deviations in the

results due to factors associated with purchasing

a car, such as car price and vehicle attributes.

We want to clarify that we do not employ all

the information from the survey; that is, we do

not treat survey answers on WTB as ‘ordinal’, but

5We note that we do not believe that our data are outdated.

Given that FAVs were not completely introduced into the

market at the time and have yet to be introduced as of 2021, a

substantial change in the results, for example, a change in sign

or implications, is unlikely. Therefore, more attention should

be paid to the signs and relative comparisons of the coeﬃcient

magnitudes of the latent constructs.

Fig. 3: Survey Structure

instead, we treat them as categorical variables.

For example, while it is possible to investigate the

result of ‘ordinal’ responses on WTB by treating 1

as not purchasing, 2 as not sure, 3 as considering

purchasing and 4 as purchasing, an increase from

1 to 2 does not necessarily indicate the probability

of a respondent purchasing an FAV. Thus, we ana-

lyze binary responses because we are interested in

whether a respondent wants to purchase an FAV.

Similarly, we do not investigate the multinomial

responses regarding WTB because we believe that

each response is independent; therefore, inves-

tigating how the decision on not to purchase

interacts with ‘no awareness’, for example, does

not ﬁt our research.

D. Identifying Disaster Victims

We ask whether the respondents have experi-

enced physical/mental damage due to NDs. If so,

we then ask them to consult a list of diﬀerent types

of disaster damage, allowing them to choose any

item that they have experienced, as illustrated in

Table I, which shows the number and proportion

of observations associated with each item in the

ﬁrst column. Among the items listed in Table I,

we place particular emphasis on those that are

likely to be a ‘traumatic experience’, which covers

experiences that are likely to be closely related

to physical and mental problems such as anxi-

ety, major depression, nightmares, hypervigilance,

and panic attacks associated with trauma-related

stimuli according to previous works in the ﬁeld of

medicine ((47), (48)).

The respondents are allowed to check multiple

items. For example, a respondent can check both

‘Severe Health Problems’ and ‘Severe Injury’. Sum-

ming up the ﬁndings from the aforementioned

works, we denote the following as ‘traumatic expe-

riences’: Entire Collapse of a Home (Item No. 1),

Home Destruction (Item No. 2), Severe Property

Damage (Item No. 3), Severe Furniture Damage

(Item No. 4.), Death of Family or Friends (Item No.

5), Severe Injury (Item No. 6), Posttraumatic Stress

Disorder (PTSD; Item No. 10), and Severe Health

Problems (Item No. 11). Consequently, we have

7,853 respondents, which is a suﬃcient number of

observations for us to conduct statistical analysis,

as presented in Table I.

E. Socioeconomic variables

To control for the respondents’ sociodemo-

graphic characteristics, we also include the follow-

ing sociodemographic variables: income, gender,

age, and number of family members. Moreover,

we include a car ownership dummy (=1 if the

respondent owns a car), commuting dummy (=1

if the respondent commutes) and license dummy

(=1 if the respondent has a driver’s license).

While we survey only those who are aware of

FAVs, it is possible that in 2017, the respondents

were less familiar with FAVs than their counter-

parts were in 2021. Thus, it is necessary to identify

the respondents who are not familiar with FAVs.

Thus, we exclude those who answer ‘I don’t know’

to all of the questions related to FAVs (5,987 ob-

servations). Finally, we exclude those who select ‘I

don’t know/I don’t want to answer’ for questions

about their individual income (30,156 observa-

tions). As a result, we have 69,391 respondents in

total.

Finally, we summarize our data in Table II,

which shows the descriptive statistics of the key

variables for the entire sample (the ﬁrst column)

and each group in the following columns. The

summary statistics in Table II indicate that the

average perceived value gap among disaster vic-

tims is lower than those of respondents who are

not disaster victims. Such a trend indicates that

those who are disaster victims are more unlikely

to appreciate FAVs after the accident information

is shared. Moreover, we do not observe substantial

diﬀerences between socioeconomic variables (e.g.,

income, age, and commuting time) between those

who have experienced NDs and those who have

not.

We do not observe substantial diﬀerences be-

tween socioeconomic variables (e.g., income, age,

and commuting time) across groups. Then, we

proceed to the comparison between the latent

variables.

F. Explanatory Factor Analysis

We identify the latent variables that can be re-

lated to FAV choices based on, whenever possible,

statements previously used and found to be eﬀec-

tive in the literature. Table III presents the names

of our latent variables, the items corresponding

to them, and a reference list. In the following two

paragraphs, we explain how we identify the latent

constructs. The survey consists of questionnaires

related to the following items:

1) RPs: Whether the respondent is risk-seeking

or risk-averse, which captures whether ex-

TABLE I: Criteria for Selecting Disaster Victims

No. Item Obs. Portion(%) Public Disaster Victims

1 Entire Home Collapse 1,474 1.47% X O

2 House Destruction 2,000 2.00% X O

3 Severe Property Damage 7,045 7.04% X O

4 Severe Furniture Damage 6,921 6.91% X O

5 Death of Family and Friends 1,137 1.14% X O

6 Severe Injury 1,019 1.02% X O

7 Evacuation 1,741 1.74% X X

8 Moving House 2,429 2.43% X X

9 Unemployment 1,369 1.37% X X

10 Posttraumatic Stress Disorder 879 0.88% X O

11 Severe Health Problems 836 0.84% X O

12 Others 3,591 3.59% X X

13 No Experience 69,647 70.92% O X

14 I Don’t Know 9,049 7.23% X X

Total 100,810 100% 69,647 21,915

Analytical Sample Total 57,105 7,853

TABLE II: Descriptive Statistics of Analytical Sample

Variable Mean Std. Dev. Min Max

Those without Disaster Experience (N=57,105)

Purchasing Intention (Before Accident) 0.593 0.687 0 2

Purchasing Intention (After Accident) 0.657 0.666 0 2

Perceived Value (10,000 JPY) 66.345 126.816 0 1,000

Gender (=1 if Female) 0.315 0.465 0 1

Individual Income (10,000 JPY) 470.094 401.723 100 3500

Age 50.747 11.17 18 99

Car Ownership (=1 if Owns a Car) 0.80 0.430 0 1

License (=1 if Owns a drivers license) 0.92 0.269 0 1

Accident Awareness 0.264 0.441 0 1

Those Disaster Victims Experience (N=7,853)

Purchasing Intention (Before Accident) 0.694 0.724 0 2

Purchasing Intention (After Accident) 0.742 0.687 0 2

Perceived Value (10,000 JPY) 76.434 143.846 0 1,000

Gender (=1 if Female) 0.272 0.445 0 1

Individual Income (10,000 JPY) 470.094 401.723 100 3500

Age 52.189 11.618 18 99

Car Ownership (=1 if Owns a Car) 0.832 0.374 0 1

License (=1 if Owns a drivers license) 0.925 0.268 0 1

Accident Awareness 0.242 0.428 0 1

posure to traﬃc accidents and NDs changes

RPs and whether such changed RPs are re-

ﬂected in the respondents’ choices of new

technologies.

2) SP: The extent to which a respondent trusts

the government, local authorities, family

and friends during emergency situations. In

other words, this item refers to whether a

respondent can expect mental support from

the above actors.

3) FAV-related questions: Awareness of FAVs,

perceived value for FAVs, WTB on FAVs,

expected beneﬁts of FAVs or merits (Tech-

savviness) and whether respondents fear

FAVs due to potential accidents (Accidents).

4) Environmental Concerns: Importance of

waste recycle (Recycle), resolving green-

house gas emissions (GHG Emission), alle-

viating air pollution (Air Pollution), and pre-

serving natural environment (Conservation).

Table III presents the list of questions and pre-

vious works to which we refer to create survey

questions according to the latent categories. First,

for RPs, using the examples that can be found

in daily routines, we select the questions that

can measure whether the respondents prefer high

levels of risks. If a respondent believes that he or

she is risk-seeking, then he or she chooses ‘Yes’

and ‘No’ otherwise.

For the SP variables, the respondents are asked

to answer questions about their beliefs in SP from

diverse authorities on a scale from 1 to 5, where

(1) indicates ‘strongly disagree’, (2) indicates ‘dis-

agree’, (3) indicates ‘neither agree nor disagree’, (4)

indicates ‘agree’ and (5) indicates ‘strongly agree’.

Next, we ask about the respondents’ concerns

regarding the environment in terms of its impor-

tance for policy. Based on previous studies, we

classify environmental policy topics into eight fac-

tors referring to the House of Councilors, National

Diet of Japan (2015). We have 11 questions in total,

and the topics cover air pollution, environmental

conservation, water pollution, endangered species

conservation (biodiversity), reuse and recycling,

waste disposal, and CO2emissions with questions

such as “How important is the policy to you?” The

scale of responses is as follows: (0) for no aware-

ness/interest at all—meaning that the diﬀerence

between those who answer (0) and others depends

on whether a person at least has an interest in a

certain policy/issue; (1) for very nonsigniﬁcant; (2)

for nonsigniﬁcant; (3) for neither important nor

nonsigniﬁcant; (4) for important; and (5) for very

important.

According to the context of the questions, we

divide the questions into importance of waste re-

cycling (Recycle), resolving greenhouse gas emis-

sions (GHG Emission), alleviating air pollution (Air

Pollution), and preserving the natural environ-

ment (Conservation) according to the relevance

of the survey item to the environmental concerns

context.

IV. M

A. Discrete-Choice Modelling

In our study, we separately identify two com-

ponents: a discrete choice model and a latent

variable model. Each of these submodels consists

of a structural component and a measurement

component. In the discrete choice component,

the alternatives’ utilities may depend on both the

observed and latent attributes of the alternatives

and characteristics of decision makers. Consistent

with the random utility maximization model, util-

ity as a theoretical construct is operationalized by

assuming that individuals choose the alternative

with the greatest utility.

For the discrete-choice part, we adopt mixed

logit referring to (60), which allows for unob-

served preference heterogeneity among respon-

dents. Mixed logit is a highly ﬂexible model that

can approximate any random utility model ((61))

and overcomes the three limitations of standard

logit by allowing for random taste variation, un-

restricted substitution patterns and correlations

between unobserved factors over time.

In our model, Let Ynj represent a discrete choice

of a person namong Jalternatives. Suppose Unj is

the utility of the jth choice to ith individual. Then,

with the deterministic utility Vnj , and random

error term nj , the utility Unj can be written as;

Unj =β0

nxnj +nj (1)

where xnj are the vector of observed variables

that relate to the alternative and the decision-

maker, βnis the vector of coeﬃcients of these

variables for person nrepresenting that person’s

tastes, and nj is a random term that is extremely

valuable.

In the mixed logit speciﬁcation, the coeﬃcient

varies over individuals in the population with

density f(β). This population density is a func-

tion of parameter θ, which represents the mean

and covariances of the vector βin the popula-

tion. In other words, this speciﬁcation is identical

for standard logit except that the βcoeﬃcient

varies across individuals rather than being ﬁxed.

We allow for one or more of the parameters in

the model to be randomly distributed and thus

allow for the coeﬃcients in the model to vary

across decision makers. In this speciﬁcation, the

individual is aware of his or her own βiand nj

for all jand chooses alternative iif and only if

Uni > Unj ∀j6=n. If βnis observed, then the

choice probability is a standard logit; therefore,

the probability conditional on βnis

Lni(βn) = eβ0

nxni

Σjeβ0

nxni (2)

However, the researcher observes xnj but not βn

or nj . Therefore, the unconditional choice prob-

ability is the integral of Lni(βn)over all possible

variables of βn:

Pni(βn) = ZPni (βn)f(βn|θ)dβn(3)

which is the mixed logit probability. In this

study, we specify the distribution f(β), following

(62) and (63), to be normal: β∼N(b, W )with

estimated parameters band W.

TABLE III: Latent Variables, Explanation and Works Referenced

Explanation Source

Latents on Risk, Disaster and Accidents

Risk Perception (RP) (24), (25), (26)

Risk-Averse 1. Do you have a comprehensive medical examination on a regular basis?

Risk-Averse 2. Do you purchase travel insurance at your own expense

when travelling domestically or abroad?

Risk-Seeking 1. Do you gamble (pachinko, horse-racing,

bicycle racing, auto racing, etc.)?

Risk-Seeking 2. Do you prefer high risk and high return to

low risk and low return when purchasing ﬁnancial products?

Social Support (SP)(49), (50), (51)

When faced with diﬃculties such as

supply, money, and housing in times of disaster,

choose the one that best describes your thoughts.

SP1. I expect physical/mental support from the government,

local authorities and public institutions.

SP2. I expect physical/mental support from family members and friends.

SP3. I expect physical/mental support from local volunteers and

members of local communities.

Latents on FAVs

Fear (FE) (52), (53), (54)

Accident 1. There is a possibility that children will be able to move it on their own.

Accident 2. There is a possibility that the software will be hacked (cybersecurity).

Accident 3. A malfunction may cause accidents.

Malfunction 1. It is unclear who is responsible for an accident due to FAV technology.

Malfunction 2. Traﬃc volume and congestion may increase because those without a license can drive.

Malfunction 3. A malfunction may take me to the wrong destination.

Merits (MR) (55), (56), (57)

Burden Reduction 1. People can drive without a license.

Burden Reduction 2. Burdens on driving will be decreased.

Burden Reduction 3. Able to do other work while driving (multitasking).

Safety 1. Children can drive a vehicle without a guardian.

Safety 2. Able to avoid responsibility for traﬃc accidents.

Environmental Concern (58), (59), (59)

GHG Emissions 1 Reducing annual greenhouse gas (GHG) emissions

GHG Emissions 2 Percentage of eco-cars out of total cars

GHG Emissions 3 The usage of renewable energy in power generation

Recycle 1 Final disposal volume of garbage and waste

Recycle 2 Cycle utilization rate (the percentage of the total amount of

reusable and recycled materials to be injected into society)

Air Pollution 1 Alleviating particulate matter (PM 2.5.) pollution is critical for our society.

Air Pollution 2 Resolving air pollution (particularly photochemical smog) is important

Conservation 1 Percentage of endangered species around

Conservation 2 Biodiversity

Conservation 3 The percentage of green area within 1,500 meters of one’s home

Conservation 4 Green purchasing: When purchasing goods and services,

I consider the environmental impact before purchasing.

B. ICLV Model Framework

In this study, we adopt the ICLV approach,

which incorporates discrete choices and latent

variables. For the discrete choice components,

the utility of the alternatives may be determined

by both their observed and latent attributes, as

well as the characteristics of the decision makers.

Utility, as a theoretical construct, is quantiﬁed by

assuming that individuals choose the alternative

with the greatest utility, which is consistent with

the random utility maximization model.

Conversely, the latent variable part is quite ﬂexi-

ble, enabling simultaneous relationships between

latent variables. The latent variables themselves

are assumed to be measured by multiple indi-

cators such as responses to Likert-scale survey

questions.

Mathematically, the model is typically repre-

sented using the following set of four equations:

Unj =β0

nxnj +γ0

nx∗

n+nj (4)

in=Dx*n+ηn(5)

Ynj =









1,if Unj ≥Unj0forj0∈1, ..., J .

0,otherwise

(6)

where Unj refers to utilities of each of the J

alternatives, as perceived by decision maker n;

xnj is the (K×1) vector of the observed ex-

planatory variables; x∗

nis the (M×1) vector of

the latent explanatory variables; βnand γnare

the vectors of model parameters denoting sen-

sitivities to the observable and latent variables,

respectively; and nj is the stochastic component

of the utility speciﬁcation. Moreover, inis the

(R×1) vector of the indicators used to measure

the latent variables; Dis the (R×M)matrix of

model parameters denoting the sensitivities of the

measurement indicators to the latent variables;

ηnis the (R×1) vector denoting the stochastic

component of the measurement equation; and Ynj

is the choice indicator, equal to one if decision

maker n chooses alternative jand zero otherwise.

The stochastic components nj and ηnare as-

sumed to be mutually independent.

The ICLV approach has a remarkable advantage

over the conventional choice model. In conven-

tional analyses of travel mode choice, the utility

is determined by tangible aspects such as travel

time and cost and socio-demographic variables

including age, gender, and income ((64)). In the

last two decades, the ICLV approach has been

proposed as a suitable way of including intangible

aspects by considering travelersâ€™ perceptions

and attitudes as determinants of choice behavior

((65)). Using ICLV models, researchers have found

that subjective attitudes have statistically signif-

icant eﬀects, and including them improves the

explanatory power of the choice model ((66), (67),

(68), (69)). Because of the advantage, ICLV models

have been frequently utilized in recent studies to

more precisely explain travel mode choice behav-

ior.

Figure 4describes the ICLV model framework.

In this setting, an individual’s choice is deter-

mined by the utility of using each travel mode,

and the utility is explained by both observed and

latent variables. The value of a latent variable

represents the strength of a certain subjective

perception of an individual (e.g., perception to-

wards social support), which is estimated from the

responses to several indicator variables connected

to the perception (e.g., how much one expects

physical/mental support from the government).

Since the latent variable is expressed as a variable

with a mean of 0 and a standard deviation of 1,

the coeﬃcient of the latent variable indicates the

change in the probability of choice when the la-

tent variable increases by one standard deviation.

V. R

In this section, we ﬁrst explain our results in

general in Subsection V-A and then demonstrate

the substitution pattern in Subsection V-B.

A. Main Results

Table IV reports the estimation results of FAV

choices before the sharing of accident-related

Fig. 4: Framework of ICLV model

news (Model (1) for those without disaster experi-

ences and Model (3) for disaster victims) and after

the sharing of accident-related news (Model (2)

for those without disaster experiences and Model

(4) for disaster victims). Panel (A) of the results

table reports the latent variables (interacted with

the disaster victims dummy); Panel (B) presents

the results for the socioeconomic variables; and

Panel (C) shows the results for the heterogeneity

coeﬃcients, constants and model ﬁt. We proceed

mainly by comparing the coeﬃcient estimates of

Models (1) and (2). Overall, we ﬁnd that SP is

pivotal for FAV choices and that such trends were

stronger for the disaster victims.

a) Notes on Interpretation.: Before interpret-

ing the results, we want to clarify that people

can have diﬀerent combinations of latent vari-

ables. For instance, people can have high levels of

‘SP’ and ‘Risk-Averse.’ For example, a lower level

of ‘Risk-Averse’ is negatively correlated with FAV

choices, which does not indicate that SP for that

individual discourages his or her choice of FAV.

Potential consumers express aﬃnity toward FAVs

if SP is given. Thus, our result shows the changes

in FAV choices following one-standard-deviation

increases in a latent variable, holding the other

latent variables ﬁxed. All regressions include pre-

fecture, year and individual ﬁxed eﬀects. We also

calculate individual-level heterogeneity, which is

noted in the "Heterogeneity Coeﬃcient" section

of Panel (C).

b) SP: SP (in Panel A-1) shows positive and

signiﬁcant coeﬃcients in all models and displays

an increasing trend after the accident news is

shared. Previous works in the ﬁeld of medicine

((70)) have revealed that SP is likely to boost the

resilience of disaster victims and that resilience is

not only the ability to deal eﬀectively with nega-

tive situations and instantly recover from negative

impacts but also mental preparedness for future

situations and vulnerabilities. Thus, our results in-

dicate that while SP is more eﬀective when people

are exposed to accidents, those who choose FAVs

are likely to have higher levels of SP. This observa-

tion is consistent with anecdotes from documents

published by the Japanese government indicating

that the support of local authorities and family

is vital for achieving inclusive transportation by

encouraging people to choose FAVs.6

c) Risk Preferences: Panel A-2 displays the

results on risk preferences, accidents and merits.

First, risk-averse individuals are likely to adopt

FAVs, while risk-seekers are unlikely to do so. The

positive coeﬃcients of risk aversion in Models (1)

and (3) indicate that risk aversion is positively

correlated with FAV adoption before the accident

news is shared, while it slightly decreases in Mod-

els (2) and (4).

The positive coeﬃcient of risk aversion and

FAV adoption is consistent with previous works;

people mostly have positive perceptions of FAVs.

Driverless vehicles (without human driving con-

trols) are perceived as safer than vehicles with no

automation ((71)). Thus, it is quite natural to infer

that risk-averse individuals do not prefer fatal

accidents and therefore are likely to avoid such

risks. In this sense, the decline in the coeﬃcient

in Models (2) and (4) is quite natural.

Likewise, FAVs are less preferred by those who

are risk seekers. The preferences of risk seekers

decrease even after the accident news is shared;

for the general public, the preference decreases

from -0.091 (Model (1)) to -0.230 (Model (2)), and

6We refer to the Japanese Cabinet’s guideline entitled “Social

Principles of Human-Centric AI" (2020).

for disaster victims, the preference decreases from

-0.084 (Model (3)) to -0.195 (Model (4)).

d) Accident and Merits: The general public

and disaster victims show indiﬀerence—a statisti-

cally insigniﬁcant coeﬃcient—before the accident

news is shared, which then decreases signiﬁcantly

after the accident news is shared. While the gen-

eral public indicates concern about such malfunc-

tion, leading to a reduction in the coeﬃcients,

disaster victims do not.

Disaster victims care about safety more than the

public does, as seen from the positive coeﬃcients

in Models (3) and (4). As expected, after the ac-

cident news is shared, a one-standard-deviation

increase in safety is likely to cause more disaster

victims to choose FAVs.

e) Environmental Perceptions: Panel A-

3 presents the estimated coeﬃcients of

environmental perceptions. Two ﬁndings are

worth discussing. First, on the one hand,

those who are satisﬁed with greenhouse gas

(GHG) emissions and air pollution before

the accident are unlikely to choose FAVs; on

the other hand, conservation shows positive

coeﬃcients across groups. Second, the statistical

signiﬁcance of environmental perceptions after

the accident news is shared mostly disappears.

This trend is stronger for disaster victims, as

some of the parameters (i.e., GHG emissions and

conservation) remain signiﬁcant for the public.

f) Socioeconomic Variables: Regarding the re-

maining parameters, as expected and consistent

with the previous works on FAV demand, socioe-

conomic attributes mostly exhibit signiﬁcant co-

eﬃcients, which implies that these characteristics

also play a role in FAV demand. For example,

perceived value, being male, being younger in age,

having higher income, being a commuter, and

having a license show higher coeﬃcients. Those

who were already aware of the accident show

negative coeﬃcients. One diﬀerence between the

public and disaster victims is that while the public

shows higher coeﬃcients when they have a car,

disaster victims do not.

g) Heterogeneity Coeﬃcients: The random co-

eﬃcient results show a signiﬁcant level of het-

erogeneity in the respondents’ preferences. Given

that the random coeﬃcients are signiﬁcant and

that these parameters help alleviate the well-

known problem of the independence of irrelevant

alternatives shown by traditional logit models, the

random coeﬃcients play a critical role in deﬁning

the substitution patterns, as explained by (72).

In summary, our demand estimation indicates

the importance of latent factors such as SP

and that disaster victims show diﬀerent trends

concerning these factors, motivating their FAV

choices. We then take the next step: we consider

those who switch their decisions after the accident

news is shared. Speciﬁcally, we analyze those who

answer “do not purchase” before the accident

news is shared but switch to “purchase” after the

accident news is shared. Do the factors fostering

FAV choices change? Does SP still motivate the

public and disaster victims to choose FAVs to

overcome negative information? We answer these

questions by examining the substitution patterns

in the next subsection V-B.

B. Substitution Patterns

Table Vshows the estimation results. Model (1)

shows the estimated coeﬃcients of those who are

not disaster victims, while Model (2) presents the

results of those disaster victims’ experiences. For

the substitution patterns, we estimate the demand

of those who answer “not purchase” before the

news regarding the accident is shared but decide

to “purchase” after the accident news is shared.

Model speciﬁcations such as ﬁxed eﬀects and

heterogeneity coeﬃcients are the same as in the

model presented in Table IV. One diﬀerence from

the main models is that we employ the perceived

value gap, a product of perceived value before

accident news is shared minus that after accident

news is shared.

Focusing on SP and risks (Panel A-1), column

1 shows that SP supports FAV adoption among

the public by 4.92% (coeﬃcient of SP), while the

impact of SP remains ambiguous to disaster vic-

tims (coeﬃcient of -0.0003). Thus, shifting disaster

victims to choose FAVs through SP may not be an

eﬀective approach. However, note that the coef-

ﬁcient of SP is statistically signiﬁcant for people

without disaster experience. Therefore, our result

still supports that SP is crucial when people shift

from non-FAVs to FAVs.

The diﬀerence between the public and disaster

victims may stem from risk preference, as dis-

played in Panel A-2. The coeﬃcients of the risk

preferences of the public are nonsigniﬁcant, while

they show negative coeﬃcients for disaster vic-

tims. Such results indicate that regardless of risk,

disaster victims’ preferences for FAVs are likely to

be discouraged if the accident news is shared.

The environmental perceptions and socioeco-

nomic variables listed in Panels A-3 and B imply

the trends of people who are likely to switch to

FAVs. For environmental perceptions, those who

are not satisﬁed with the conservation of the

natural environment and those who are satisﬁed

with the reduction in waste and GHG emissions

(public) are likely to switch to FAVs after the acci-

dent news is shared. The rest of the parameters are

consistent with the main result in Table IV, while

accident awareness shows a positive coeﬃcient.

Thus, those who decide to switch to FAVs after the

accident news is shared are likely to be aware of

potential accident concerns. For heterogeneity, we

conﬁrm the individual heterogeneities of 0.1354

from Model (1) and 0.1227 from Model (2), which

are signiﬁcant.

VI. O D

FAVs are not yet introduced in the market in

any part of the world.7Therefore, people would

have a conceptual sketch of how FAVs work and

would consider whether they would purchase an

AV according to that impression. Therefore, com-

pared to the actual technical level, the perception

of FAVs would not vary signiﬁcantly by country.

Then we must control for the diﬀerences in

how people perceive FAVs. Thus, we include the

possible factors that can aﬀect people’s percep-

tions (latent) in the model. To do so, we identify

possible factors that can aﬀect people’s percep-

tions (latent) by searching through the previous

works and found the following latent factors: risk

perceptions, concerns about accidents, merits, en-

vironmental concerns, and social support.

Thus, 1) because our study is based on how

people perceive FAVs rather than the actual tech-

nical level, and 2) because we control for people’s

perceptions through latent variables, substantial

changes in the results due to technological diﬀer-

ences are less likely to happen across other coun-

tries. More attention can be given to the signs and

relative comparisons of coeﬃcient magnitudes of

the latent constructs.

A. Policy Implications

Providing governmental (or local institutional)

guidelines on the prior knowledge of FAVs would

signiﬁcantly alleviate the negative inﬂuence of

concerns associated with the locus of losing con-

trol. Most of the obstacles that hinder disaster

7This is not due to a lack of technological innovations but

more related to legal issues, particularly concerning responsi-

bility in times of accidents.

TABLE IV: Main Estimation Result

Model (1) Model (2) Model (3) Model (4)

Before Accident Accident Accident Before Accident Accident Accident

Without Disaster Disaster Victims Without Disaster Disaster Victims

Coeﬃcient Std. Err Coeﬃcient Std. Err Coeﬃcient Std. Err Coeﬃcient Std. Err

Panel (A): Latents

Panel (A-1): Social Support

Social Support (SP) 0.101*** 0.013 0.172*** 0.026 0.111*** .013 0.163*** .028

Panel (A-2): Risk, Accident and Merits

Risk-Averse 0.214*** 0.023 0.166*** 0.049 0.175*** 0.023 0.148*** 0.051

Risk-Seeking -0.091*** 0.027 -0.230*** 0.053 -0.084*** 0.028 -0.195*** 0.056

Accident -0.006 0.056 -0.427*** 0.121 0.015 0.058 -0.216* 0.127

Malfunction -0.038 0.056 -0.417*** 0.122 -0.002 0.058 -0.171 0.128

Safety -0.015 0.025 0.019 0.053 0.057** 0.027 0.128** 0.057

Burden Reduction 0.377*** 0.029 0.308*** 0.062 0.298*** 0.030 0.248*** 0.065

Panel (A-3): Environmental Perceptions

Recycle 0.020 0.021 0.004 0.041 0.010 0.022 -0.001 0.043

GHG Emission -0.059*** 0.018 -0.071** 0.035 -0.037** 0.018 -0.029 0.037

Air Pollution -0.109*** 0.017 -0.026 0.034 -0.090*** 0.018 0.001 0.036

Conservation 0.037* 0.020 0.077* 0.040 0.037* 0.021 0.060 0.042

Panel (B): Socio-Economic Variables

ln Perceived Value 0.305*** 0.051 0.385*** 0.052 0.328*** 0.054 0.360*** 0.061

Female -0.257*** 0.026 -0.252*** 0.056 -0.125*** 0.027 -0.125** 0.058

ln Age -0.362*** 0.047 -0.634*** 0.100 -0.237*** 0.048 -0.437*** 0.106

ln Income 0.162*** 0.015 0.079** 0.031 0.117*** 0.016 0.045 0.033

Commuter 0.055** 0.024 0.019 0.051 0.037 0.025 0.045 0.053

Car Ownership 0.233*** 0.027 0.133** 0.060 -0.102*** 0.028 -0.143** 0.064

Accident Awareness -0.401*** 0.022 -0.347*** 0.048 -0.295*** 0.022 -0.225*** 0.050

License 0.572*** 0.040 0.665*** 0.086 0.234*** 0.040 0.225** 0.088

Panel (C): Others

Heterogeneity Coeﬃcient 0.361*** 0.090 0.262*** 0.083 0.470*** 0.099 0.423*** 0.119

Constant -1.198*** 0.326 0.947 0.579 -0.424 0.341 1.150* 0.621

Log-Likelihood -40,467.62 -38,410.474

Note: Standard errors in parentheses. * p<0.1, ** p<0.05, *** p<0.01.

N=69,391

N of Without Disaster=57,105

N of Disaster Victims=12,286

Prefecture Fixed Eﬀects are included.

victims from choosing FAVs derive from the fear of

losing control, particularly in times of accidents,

which thus renders disaster victims more fearful

than others of new technology ((73), (74)). In that

sense, our study reaﬃrms the ﬁndings of past

studies that have noted the impact of prior knowl-

edge on attitudes toward new types of vehicles

(e.g., electric cars) and on intentions to use them

((75)). Such a ﬁnding would apply to both disaster

victims and people without disaster experiences.

According to previous medical studies on trau-

matic symptoms ((76)), family and peer sup-

port can encourage disaster victims to adapt to

new technologies or return to driving. Rather

than hastily pushing disaster victims to adopt

and appreciate FAVs, gradually letting them adopt

FAV usage through repeated short-term travel or

simulation-based training with the support of

family and friends is desirable. Such a conclusion

also implies that social support can work as a

stimulus for disaster victims to overcome the fear

of disasters, which hinders them from choosing

FAVs.

B. Managerial Implications

a) SPs: Our results show that SP plays a criti-

cal role in persuading both the public and disaster

victims to choose FAVs. This ﬁnding is consistent

with previous works showing that family encour-

agement is a suitable stimulus for socially vulner-

able people to accept new technologies ((77)) and

extends the works of (78) and (79), which incorpo-

rate the eﬀect of social interaction into the choice

TABLE V: Substitution Result

Model (1) Model (2)

Without Disaster Disaster Victims

Coeﬃcient Std. Err Coeﬃcient Std. Err

Panel (A): Latents

Panel (A-1): Social Support

Social Support (SP) 0.0492*** 0.0126 -0.0003 0.0262

Panel (A-2): Risk, Accident and Merits

Risk-Averse -0.0130 0.0226 -0.0947* 0.0489

Risk-Seeking 0.0282 0.0270 -0.1355*** 0.0526

Accident -0.2403*** 0.0563 -0.3588*** 0.1220

Malfunction 0.2039*** 0.0569 0.3113** 0.1229

Safety 0.0760*** 0.0253 0.1047** 0.0529

Burden Reduction -0.0978*** 0.0294 -0.1501** 0.0628

Panel (A-3): Environmental Perceptions

Recycle 0.0444** 0.0213 0.0716* 0.0415

GHG Emissions 0.0694*** 0.0180 0.0416 0.0358

Air Pollution -0.0168 0.0173 0.0282 0.0344

Conservation -0.0893*** 0.0203 -0.1198*** 0.0399

Panel (B): Socio-Economic Variables

ln Perceived Value 0.0146 0.0296 0.0224 0.0400

Female 0.3071*** 0.0253 0.3025*** 0.0547

ln Age 0.1314*** 0.0458 0.1800* 0.0978

ln Income -0.0785*** 0.0149 -0.0485 0.0311

Commuter -0.0778*** 0.0238 -0.1446*** 0.0500

Car Ownership -0.3311*** 0.0257 -0.2131*** 0.0587

Accident Awareness 0.3967*** 0.0211 0.4583*** 0.0465

License -0.2413*** 0.0360 -0.3630*** 0.0787

Panel (C): Others

Heterogeneity Coeﬃcient 0.1354*** 0.0376 0.1227*** 0.0420

Constant -0.0668 0.2396 -0.8221 0.5357

Log-Likelihood -40,467.62 -38,410.474

Note: Standard errors in parentheses. * p<0.1, ** p<0.05, *** p<0.01.

N=69,391

Prefecture Fixed Eﬀects are Included.

model. Speciﬁcally, this work contributes by 1)

examining disaster victims and 2) including other

latent variables associated with risk preferences,

accidents, merits, and environmental concerns.

While our results show that SP encourages people

without disaster experiences to shift to choose

FAVs, we ﬁnd relatively weak evidence for such

a trend for disaster victims.

Thus, how can disaster victims be encouraged

to shift to FAVs? Promoting the practical beneﬁts—

safety—would help with this shift. The coeﬃcient

of safety for disaster victims is higher, positive,

and signiﬁcant. At the same time, the coeﬃcient

of accidents for disaster victims is negative, indi-

cating that disaster victims are afraid of potential

accidents. Therefore, increasing the reliability of

government entities and providing safety guide-

lines may attract both disaster victims and the

public because our results still show that SP plays

a role in public perception. Thus, our results do

not indicate that promoting SP is futile; neverthe-

less, SP is a critical factor in encouraging people—

those without disaster experience—to choose or

substitute with FAVs. Therefore, there is still a

need for policies that ensure SP as a viable so-

lution for boosting FAV adoption.

b) Risk Preferences: Another interesting ﬁnd-

ing from this research is that we reveal that people

regard FAVs as a safe option. Risk-averse indi-

viduals prefer FAVs, and their preference declines

when faced with the news of the accident. There-

fore, from a policy implication perspective, in line

with SP, providing governmental support to design

and implement regulations on safety—which fos-

ters trust in FAVs—is recommended. These policy

measures fundamentally include the risk realloca-

tion process ((24)).

c) Environmental Perspective: With regard to

other types of parameters, while the previous

works have revealed that a higher level of environ-

mental concern motivates FAV adoption, interest-

ingly, our results reveal that diﬀerent types of envi-

ronmental perceptions may have diﬀerent impli-

cations. Our results emphasize the importance of

separately identifying environmental perceptions

according to context. Another interesting ﬁnding

from our results is that the coeﬃcient magnitudes

of environmental perceptions are likely to disap-

pear after the accident news is shared, indicating

that people prioritize safety and practical beneﬁts

over environmental perceptions. This result does

not imply that environmental perception is less

important; the concern of previous works and in-

dustry reports has been that FAVs might increase

the total vehicle miles traveled (VMT), as they

allow many people to travel freely (Center for

American Progress, 2016).

The impact of FAVs on energy use and emissions

largely depends on their eﬀect on the total VMT,

their fuel eﬃciency and their fossil fuel consump-

tion. For example, Stephens et al. (2016) estimated

that the highest VMT increase and the smallest

eﬃciency increase could result in a 205 percent

increase in US transportation energy use. These

concerns again indicate the importance of envi-

ronmental perceptions, which make drivers aware

of their driving distances and the consequences of

their driving to the environment. As mentioned

in (16), another way to reduce emissions and

energy usage is to promote shared FAVs and elec-

tric FAVs and manage road infrastructures. Such

policies would be more eﬀective if environmental

perceptions were promoted, as environmentally

concerned consumers are more likely to adopt

such policies.

VII. C

Achieving inclusive transportation requires ac-

commodating socially vulnerable people who can-

not drive unassisted. Moreover, because accidents

are inevitable, it is necessary to estimate demand

considering situations that occur after accidents.

Using a unique survey design and a large sample

size, our study discovers that SP is pivotal in mo-

tivating disaster victims to adopt and appreciate

FAVs. We ﬁnd additional evidence on the relation-

ship between other psychometric and socioeco-

nomic factors and FAV demand. By considering

the individual-level demographic characteristics

and psychometric attributes of SP, risk prefer-

ence, environmental perceptions, accident con-

cerns, and merits, we construct a discrete-choice

model that considers individual heterogeneity. By

including such characteristics, our results have

implications for the design of eﬀective policy in-

struments and information campaigns that appeal

to disaster victims. Therefore, our ﬁndings provide

clear, practical contributions.

Our results are straightforward. The social sup-

port of family, friends, and local authorities is

a crucial factor in motivating people to appre-

ciate and purchase FAVs. To prove this, we pro-

vide quantitative evidence of newly introduced

FAVs with an additional focus on natural disaster

victims–people who have become physically or

mentally challenged due to severe disaster dam-

age, including those with post-traumatic stress

disorder. Additionally, by comparing how respon-

dents react before/after accident news is shared

with them, we reconﬁrm the importance of social

support.

Our empirical framework can be extended to

a global context, as many countries are keen to

achieve inclusive transportation and encourage

people to choose FAVs. Ultimately, by illuminating

the importance of social support on FAV choices,

our study suggests the importance of providing

prior knowledge through institutional guidelines

and enticing disaster victims to choose FAVs by

increasing support from family and friends. Doing

so could allow FAVs to be inclusive in the future.

We conclude by suggesting the following policy

guidelines: increasing the reliability of govern-

ment entities and providing safety guidelines may

encourage both disaster victims and the public to

adopt FAVs because our results still show that SP

plays a role in the perceptions of the public.

Our study is based on short-term shock, and

the provided implications are short-term based.

A possible future study examining the long-term

eﬀect of accident news would require panel data,

for example, of several years after such news is

shared. Unfortunately, we lack the data necessary

for such analysis. Thus, future research into the

long-term eﬀects of accident news is worth pur-

suing. This is because even though the Tesla acci-

dent can have long-term negative eﬀects such as

damages to the company’s reputation, we cannot

determine how long the fear of the accident would

last. For example, the fear due to the accident

could decrease (or increase) as time passes.

This paper calls for further studies related to

FAV adoption. As an example, future studies could

examine the diﬀerent aspects of SP in the FAV con-

text. The ﬁndings of this study suggest practical

policy guidelines by illuminating the importance

of SP. However, it would be meaningful to investi-

gate the behavioral mechanisms of how diﬀerent

types of SP inﬂuence FAV adoption. For example,

governments could provide SP in the form of

ﬁnancial subsidies, thereby reducing RPs. Overall,

because FAVs will substantially impact the future

of transportation systems and it is necessary to

include those who are socially vulnerable, the

interplay between FAVs and SP is a fertile area for

future research.

Acknowledgements

Our special thanks go to Seilin Uhm, University

College London, Institute of Education,

Kyungwoong Koh, Faculty of Economics, Johns

Hopkins University, Kohei Kawasaki and Thierry

Coulibaly, Urban Institute, Kyushu University,

and the seminar participants at the University of

Tokyo ( Japan) and Kyushu University (Japan) for

their helpful comments. This work was supported

by JSPS KAKENHI Grant Number JP20H00648.

R

[1] M. A. Figliozzi, “Carbon emissions

reductions in last mile and grocery

deliveries utilizing air and ground au-

tonomous vehicles,” Transportation Research

Part D: Transport and Environment,

vol. 85, p. 102443, 2020. [Online].

Available: https://www.sciencedirect.com/

science/article/pii/S1361920920306301

[2] Z. Le Hong and N. Zimmerman,

“Air quality and greenhouse gas im-

plications of autonomous vehicles in

vancouver, canada,” Transportation Research

Part D: Transport and Environment,

vol. 90, p. 102676, 2021. [Online].

Available: https://www.sciencedirect.com/

science/article/pii/S1361920920308609

[3] S. M. Mousavi, O. A. Osman, D. Lord, K. K.

Dixon, and B. Dadashova, “Investigating the

safety and operational beneﬁts of mixed traf-

ﬁc environments with diﬀerent automated

vehicle market penetration rates in the prox-

imity of a driveway on an urban arterial,”

Accident Analysis & Prevention, vol. 152, p.

105982, mar 2021.

[4] S. Tscharaktschiew and C. Evangelinos,

“Pigouvian road congestion pricing un-

der autonomous driving mode choice,”

Transportation Research Part C: Emerging

Technologies, vol. 101, pp. 79–95, 2019. [On-

line]. Available: https://www.sciencedirect.

com/science/article/pii/S0968090X18315328

[5] M. D. Simoni, K. M. Kockelman, K. M.

Gurumurthy, and J. Bischoﬀ, “Congestion

pricing in a world of self-driving vehicles:

An analysis of diﬀerent strategies in

alternative future scenarios,” Transportation

Research Part C: Emerging Technologies,

vol. 98, pp. 167–185, 2019. [Online].

Available: https://www.sciencedirect.com/

science/article/pii/S0968090X1830370X

[6] T. Li, F. Guo, R. Krishnan, A. Sivakumar, and

J. Polak, “Right-of-way reallocation for mixed

ﬂow of autonomous vehicles and human

driven vehicles,” Transportation Research

Part C: Emerging Technologies, vol. 115, p.

102630, jun 2020.

[7] C. Laurell and S. Christian, “Social media

analytics as an enabler for external search

and open foresightx2014;the case of tes-

lax0027;s autopilot and regulatory scrutiny of

autonomous driving,” IEEE Transactions on

Engineering Management, vol. 69, no. 2, pp.

564–571, 2022.

[8] S. Karnouskos, “Self-driving car acceptance

and the role of ethics,” IEEE Transactions on

Engineering Management, vol. 67, no. 2, pp.

252–265, 2020.

[9] X. Zhao, Z. Wang, Z. Xu, Y. Wang, X. Li,

and X. Qu, “Field experiments on longitu-

dinal characteristics of human driver be-

havior following an autonomous vehicle,”

Transportation Research Part C: Emerging

Technologies, vol. 114, pp. 205–224, may

2020.

[10] M. Hagl and D. R. Kouabenan, “Safe

on the road – does advanced driver-

assistance systems use aﬀect road risk

perception?” Transportation Research Part

F: Traﬃc Psychology and Behaviour,

vol. 73, pp. 488–498, 2020. [Online].

Available: https://www.sciencedirect.com/

science/article/pii/S1369847820304782

[11] S. YOO, K. Junya, K. Yuta, and M. Shunsuke,

“Achieving Inclusive Transportation: Fully

Automated Vehicles with Social Support,”

Research Institute of Economy, Trade and

Industry (RIETI), Discussion papers 22017,

Mar. 2022. [Online]. Available: https://ideas.

repec.org/p/eti/dpaper/22017.html

[12] M. Warschauer, The MIT Press. Technology

and Social Inclusion: Rethinking the Digital

Divide, 09 2004. [Online]. Available: https:

//doi.org/10.7551/mitpress/6699.001.0001

[13] Z. Han, X. Lu, E. I. Hörhager, and J. Yan,

“The eﬀects of trust in government on earth-

quake survivors’ risk perception and pre-

paredness in china,” Natural Hazards, vol. 86,

p. 437–452, dec 2016.

[14] A. Joshi and M. Aoki, “The role of

social capital and public policy in disaster

recovery: A case study of tamil nadu state,

india,” International Journal of Disaster Risk

Reduction, vol. 7, pp. 100–108, 2014. [On-

line]. Available: https://www.sciencedirect.

com/science/article/pii/S2212420913000460

[15] A. Vij and J. L. Walker, “How, when and

why integrated choice and latent variable

models are latently useful,” Transportation

Research Part B: Methodological,

vol. 90, pp. 192–217, 2016. [Online].

Available: https://www.sciencedirect.com/

science/article/pii/S019126151630234X

[16] D. J. Fagnant and K. Kockelman, “Preparing a

nation for autonomous vehicles: opportuni-

ties, barriers and policy recommendations,”

Transportation Research Part A: Policy and

Practice, vol. 77, pp. 167–181, 2015. [On-

line]. Available: https://www.sciencedirect.

com/science/article/pii/S0965856415000804

[17] N. Adnan, S. Md Nordin, M. A. bin Bahrud-

din, and M. Ali, “How trust can drive forward

the user acceptance to the technology? in-

vehicle technology for autonomous vehicle,”

Transportation Research Part A: Policy and

Practice, vol. 118, pp. 819–836, 2018. [On-

line]. Available: https://www.sciencedirect.

com/science/article/pii/S0965856418309030

[18] J. Zhu and I. Tasic, “Safety analysis of free-

way on-ramp merging with the presence of

autonomous vehicles,” Accident Analysis &

Prevention, vol. 152, p. 105966, mar 2021.

[19] C. Mao, Y. Liu, and Z.-J. M. Shen, “Dispatch

of autonomous vehicles for taxi services:

A deep reinforcement learning approach,”

Transportation Research Part C: Emerging

Technologies, vol. 115, p. 102626, jun 2020.

[20] R. Krueger, T. H. Rashidi, and J. M. Rose,

“Preferences for shared autonomous ve-

hicles,” Transportation Research Part C:

Emerging Technologies, vol. 69, pp. 343–355,

aug 2016.

[21] M. A. Moore, P. S. Lavieri, F. F. Dias, and

C. R. Bhat, “On investigating the potential

eﬀects of private autonomous vehicle use on

home/work relocations and commute times,”

Transportation Research Part C: Emerging

Technologies, vol. 110, pp. 166–185, jan 2020.

[22] K. Faber and D. van Lierop, “How

will older adults use automated vehicles?

assessing the role of avs in overcoming

perceived mobility barriers,” Transportation

Research Part A: Policy and Practice,

vol. 133, pp. 353–363, 2020. [Online].

Available: https://www.sciencedirect.com/

science/article/pii/S0965856419312091

[23] A. Nunes, B. Reimer, and J. F. Coughlin.,

“People must retain control of autonomous

vehicles,” Nature, vol. 556, pp. 169–171, 2018.

[24] S. Wang and J. Zhao, “Risk preference

and adoption of autonomous vehicles,”

Transportation Research Part A: Policy and

Practice, vol. 126, pp. 215–229, 2019. [On-

line]. Available: https://www.sciencedirect.

com/science/article/pii/S0965856417310315

[25] R. Shabanpour, N. Golshani,

A. Shamshiripour, and A. K. Mohammadian,

“Eliciting preferences for adoption of fully au-

tomated vehicles using best-worst analysis,”

Transportation Research Part C: Emerging

Technologies, vol. 93, pp. 463–478, 2018. [On-

line]. Available: https://www.sciencedirect.

com/science/article/pii/S0968090X18308957

[26] N. Huijts, E. Molin, and L. Steg,

“Psychological factors inﬂuencing sustain-

able energy technology acceptance: A

review-based comprehensive framework,”

Renewable and Sustainable Energy Reviews,

vol. 16, no. 1, pp. 525–531, 2012. [On-

line]. Available: https://www.sciencedirect.

com/science/article/pii/S136403211100428X

[27] P. Schweizer-Ries, “Energy sustainable

communities: Environmental psychological

investigations,” Energy Policy, vol. 36,

no. 11, pp. 4126–4135, 2008, transition

towards Sustainable Energy Systems. [On-

line]. Available: https://www.sciencedirect.

com/science/article/pii/S030142150800311X

[28] R. Wüstenhagen, M. Wolsink, and M. J.

Bürer, “Social acceptance of renewable

energy innovation: An introduction to

the concept,” Energy Policy, vol. 35,

no. 5, pp. 2683–2691, 2007. [Online].

Available: https://www.sciencedirect.com/

science/article/pii/S0301421506004824

[29] M.-C. Ho, D. Shaw, S. Lin, and Y.-

C. Chiu, “How do disaster characteristics

inﬂuence risk perception?” Risk Analysis,

vol. 28, no. 3, pp. 635–643, 2008. [Online].

Available: https://onlinelibrary.wiley.com/

doi/abs/10.1111/j.1539-6924.2008.01040.x

[30] M. M. Haddak, M. Lefèvre, and N. Havet,

“Willingness-to-pay for road safety improve-

ment,” Transportation Research Part A: Policy

and Practice, vol. 87, pp. 1–10, 2016. [On-

line]. Available: https://www.sciencedirect.

com/science/article/pii/S0965856416000197

[31] N. C. Bronfman, P. C. Cisternas, E. López-

Vázquez, and L. A. Cifuentes, “Trust and risk

perception of natural hazards: implications

for risk preparedness in chile,” Natural

Hazards, vol. 81, no. 1, pp. 307–327, Mar

2016. [Online]. Available: https://doi.org/10.

1007/s11069-015-2080-4

[32] T. Kato, “Sharing experience by residents

of past disaster sites: Eﬀects of time

from disasters and types of experience,”

International Journal of Disaster Risk

Reduction, vol. 61, p. 102390, 2021. [On-

line]. Available: https://www.sciencedirect.

com/science/article/pii/S2212420921003514

[33] V. Mitchell, “Consumer perceived risk:

conceptualisations and models,” European

Journal of Marketing, vol. 33, no. 1/2,

pp. 163–195, Jan 1999. [Online]. Available:

https://doi.org/10.1108/03090569910249229

[34] C. Chen, H. Nelson, X. Xu, G. Bonilla, and

N. Jones, “Beyond technology adoption:

Examining home energy management

systems, energy burdens and climate change

perceptions during covid-19 pandemic,”

Renewable and Sustainable Energy Reviews,

vol. 145, p. 111066, 2021. [Online].

Available: https://www.sciencedirect.com/

science/article/pii/S1364032121003543

[35] P. Murray-Tuite, K. Wernstedt, and W. Yin,

“Behavioral shifts after a fatal rapid

transit accident: A multinomial logit

model,” Transportation Research Part

F: Traﬃc Psychology and Behaviour,

vol. 24, pp. 218–230, 2014. [Online].

Available: https://www.sciencedirect.com/

science/article/pii/S1369847814000527

[36] C. Măirean, “Fear and avoidance of

driving among drivers involved in a

road traﬃc crash. the role of traumatic

fear and driving cognitions,” Transportation

Research Part F: Traﬃc Psychology and

Behaviour, vol. 74, pp. 322–329, 2020. [On-

line]. Available: https://www.sciencedirect.

com/science/article/pii/S1369847820305143

[37] M. Aghababaei, S. B. Costello, and

P. Ranjitkar, “Transportation impact assess-

ment following a potential alpine fault

earthquake in new zealand,” Transportation

Research Part D: Transport and Environment,

vol. 87, p. 102511, 2020. [Online].

Available: https://www.sciencedirect.com/

science/article/pii/S1361920920306982

[38] X. Demeulenaere, “How challenges of

human reliability will hinder the de-

ployment of semi-autonomous vehicles,”

Technological Forecasting and Social Change,

vol. 157, p. 120093, 2020. [Online].

Available: https://www.sciencedirect.com/

science/article/pii/S0040162520309197

[39] T. Zhang, D. Tao, X. Qu, X. Zhang,

R. Lin, and W. Zhang, “The roles

of initial trust and perceived risk in

public’s acceptance of automated vehicles,”

Transportation Research Part C: Emerging

Technologies, vol. 98, pp. 207–220, 2019. [On-

line]. Available: https://www.sciencedirect.

com/science/article/pii/S0968090X18308398

[40] Z. Xu, K. Zhang, H. Min, Z. Wang,

X. Zhao, and P. Liu, “What drives people

to accept automated vehicles? ﬁndings

from a ﬁeld experiment,” Transportation

Research Part C: Emerging Technologies,

vol. 95, pp. 320–334, 2018. [Online].

Available: https://www.sciencedirect.com/

science/article/pii/S0968090X18302316

[41] R. P. Monteiro, G. L. de Holanda Coelho,

P. H. Hanel, C. E. Pimentel, and V. V.

Gouveia, “Personality, dangerous driving,

and involvement in accidents: Testing a

contextual mediated model,” Transportation

Research Part F: Traﬃc Psychology and

Behaviour, vol. 58, pp. 106–114, 2018. [On-

line]. Available: https://www.sciencedirect.

com/science/article/pii/S1369847818300184

[42] D. Merecz, M. Waszkowska, and A. Wezyk,

“Psychological consequences of trauma

in mva perpetrators – relationship

between post-traumatic growth, ptsd

symptoms and individual characteristics,”

Transportation Research Part F: Traﬃc

Psychology and Behaviour, vol. 15,

no. 5, pp. 565–574, 2012. [Online].

Available: https://www.sciencedirect.com/

science/article/pii/S1369847812000526

[43] W.-T. Lai and C.-F. Chen, “Behavioral

intentions of public transit passengers—the

roles of service quality, perceived value,

satisfaction and involvement,” Transport

Policy, vol. 18, no. 2, pp. 318–325, 2011. [On-

line]. Available: https://www.sciencedirect.

com/science/article/pii/S0967070X10001125

[44] J. de Oña and R. de Oña, “Quality

of service in public transport based on

customer satisfaction surveys: A review and

assessment of methodological approaches,”

Transportation Science, vol. 49, no. 3,

pp. 605–622, 2015. [Online]. Available:

https://doi.org/10.1287/trsc.2014.0544

[45] P. N. E. Chee, Y. O. Susilo, and Y. D.

Wong, “Determinants of intention-to-use

ﬁrst-/last-mile automated bus service,”

Transportation Research Part A: Policy and

Practice, vol. 139, pp. 350–375, 2020. [On-

line]. Available: https://www.sciencedirect.

com/science/article/pii/S0965856420306108

[46] C. Gkartzonikas and K. Gkritza, “What have

we learned? a review of stated preference

and choice studies on autonomous vehicles,”

Transportation Research Part C: Emerging

Technologies, vol. 98, pp. 323–337, jan 2019.

[47] C. Lonigan, M. Shannon, A. Finch,

T. Daugherty, and C. Taylor, “Children’s

reactions to a natural disaster: symptom

severity and degree of exposure,” Advances

in Behaviour Research and Therapy, vol. 13,

no. 3, pp. 135–154, 1991, impact of Natural

Disasters on children and families. [On-

line]. Available: https://www.sciencedirect.

com/science/article/pii/014664029190002R

[48] C. S. North, A. Kawasaki, E. L. Spitznagel,

and B. A. Hong, “The course of ptsd,

major depression, substance abuse, and

somatization after a natural disaster,” The

Journal of Nervous and Mental Disease,

vol. 192, no. 12, 2004. [Online]. Available:

https://journals.lww.com/jonmd/Fulltext/

2004/12000/The_Course_of_PTSD,_Major_

Depression,_Substance.4.aspx

[49] M. Aklin, P. Bayer, S. Harish, and

J. Urpelainen, “Economics of household

technology adoption in developing countries:

Evidence from solar technology adop-

tion in rural india,” Energy Economics,

vol. 72, pp. 35–46, 2018. [Online].

Available: https://www.sciencedirect.com/

science/article/pii/S0140988318300690

[50] J. Axsen and K. S. Kurani, “Inter-

personal inﬂuence in the early plug-

in hybrid market: Observing social

interactions with an exploratory multi-

method approach,” Transportation Research

Part D: Transport and Environment,

vol. 16, no. 2, pp. 150–159, 2011. [On-

line]. Available: https://www.sciencedirect.

com/science/article/pii/S1361920910001525

[51] J. Jansson, T. Pettersson, A. Mannberg,

R. Brännlund, and U. Lindgren,

“Adoption of alternative fuel vehi-

cles: Inﬂuence from neighbors, family

and coworkers,” Transportation Research

Part D: Transport and Environment,

vol. 54, pp. 61–73, 2017. [Online].

Available: https://www.sciencedirect.com/

science/article/pii/S1361920915302534

[52] T. Liljamo, H. Liimatainen, and M. Pöllänen,

“Attitudes and concerns on automated

vehicles,” Transportation Research Part

F: Traﬃc Psychology and Behaviour,

vol. 59, pp. 24–44, 2018. [Online].

Available: https://www.sciencedirect.com/

science/article/pii/S1369847818303292

[53] S.-A. Kaye, I. Lewis, S. Forward, and

P. Delhomme, “A priori acceptance of

highly automated cars in australia,

france, and sweden: A theoretically-

informed investigation guided by the

tpb and utaut,” Accident Analysis &

Prevention, vol. 137, p. 105441, 2020. [On-

line]. Available: https://www.sciencedirect.

com/science/article/pii/S0001457519307390

[54] T. Zhang, D. Tao, X. Qu, X. Zhang, J. Zeng,

H. Zhu, and H. Zhu, “Automated vehicle ac-

ceptance in china: Social inﬂuence and initial

trust are key determinants,” Transportation

Research Part C: Emerging Technologies,

vol. 112, pp. 220–233, 2020. [Online].

Available: https://www.sciencedirect.com/

science/article/pii/S0968090X19312537

[55] F. Zhou, Z. Zheng, J. Whitehead,

S. Washington, R. K. Perrons, and L. Page,

“Preference heterogeneity in mode choice for

car-sharing and shared automated vehicles,”

Transportation Research Part A: Policy and

Practice, vol. 132, pp. 633–650, 2020. [On-

line]. Available: https://www.sciencedirect.

com/science/article/pii/S0965856419307402

[56] E. Molin, K. Adjenughwure, M. de

Bruyn, O. Cats, and P. Warﬀemius,

“Does conducting activities while traveling

reduce the value of time? evidence

from a within-subjects choice experiment,”

Transportation Research Part A: Policy and

Practice, vol. 132, pp. 18–29, 2020. [On-

line]. Available: https://www.sciencedirect.

com/science/article/pii/S0965856419307451

[57] Z. Wadud, D. MacKenzie, and P. Leiby, “Help

or hindrance? the travel, energy and carbon

impacts of highly automated vehicles,”

Transportation Research Part A: Policy and

Practice, vol. 86, pp. 1–18, 2016. [On-

line]. Available: https://www.sciencedirect.

com/science/article/pii/S0965856415002694

[58] J. Wu, H. Liao, J.-W. Wang, and T. Chen, “The

role of environmental concern in the public

acceptance of autonomous electric vehicles:

A survey from china,” Transportation

Research Part F: Traﬃc Psychology and

Behaviour, vol. 60, pp. 37–46, 2019. [On-

line]. Available: https://www.sciencedirect.

com/science/article/pii/S1369847818303425

[59] P. Kopelias, E. Demiridi, K. Vogiatzis, A. Sk-

abardonis, and V. Zaﬁropoulou, “Connected

and autonomous vehicles-environmental

impacts-a review,” Science of The Total

Environment, vol. 712, p. 135237, 2020. [On-

line]. Available: https://www.sciencedirect.

com/science/article/pii/S0048969719352295

[60] K. E. Train, “Recreation demand models

with taste diﬀerences over people,” Land

Economics, vol. 74, no. 2, pp. 230–239,

1998. [Online]. Available: http://www.jstor.

org/stable/3147053

[61] D. McFadden and K. Train, “Mixed mnl mod-

els for discrete response,” Journal of Applied

Econometrics, vol. 15, no. 5, pp. 447–470,

2000.

[62] D. Revelt and K. Train, “Mixed Logit with

Repeated Choices: Households’ Choices of

Appliance Eﬃciency Level,” The Review of

Economics and Statistics, vol. 80, no. 4,

pp. 647–657, 11 1998. [Online]. Available:

https://doi.org/10.1162/003465398557735

[63] M. Ben-Akiva, D. Mcfadden, M. Abe,

U. Böckenholt, D. Bolduc, D. Gopinath,

T. Morikawa, V. Ramaswamy, V. Rao,

D. Revelt, and D. Steinberg, “Modeling

methods for discrete choice analysis,”

Marketing Letters, vol. 8, no. 3, pp.

273–286, Jul 1997. [Online]. Available:

https://doi.org/10.1023/A:1007956429024

[64] M. E. Ben-Akiva, S. R. Lerman, S. R. Lerman

et al., Discrete choice analysis: theory and

application to travel demand. MIT press,

1985, vol. 9.

[65] M. Ben-Akiva, D. McFadden, K. Train,

J. Walker, C. Bhat, M. Bierlaire, D. Bolduc,

A. Boersch-Supan, D. Brownstone, D. S.

Bunch et al., “Hybrid choice models: Progress

and challenges,” Marketing Letters, vol. 13,

no. 3, pp. 163–175, 2002.

[66] M. V. Johansson, T. Heldt, and P. Johansson,

“The eﬀects of attitudes and personality traits

on mode choice,” Transportation Research

Part A: Policy and Practice, vol. 40, no. 6, pp.

507–525, 2006.

[67] Á. Fernández-Heredia, S. Jara-Díaz, and

A. Monzón, “Modelling bicycle use inten-

tion: the role of perceptions,” Transportation,

vol. 43, no. 1, pp. 1–23, 2016.

[68] M. F. Yáñez, S. Raveau, and J. d. D. Or-

túzar, “Inclusion of latent variables in mixed

logit models: modelling and forecasting,”

Transportation Research Part A: Policy and

Practice, vol. 44, no. 9, pp. 744–753, 2010.

[69] H. Link, “Is car drivers’ response to conges-

tion charging schemes based on the correct

perception of price signals?” Transportation

Research Part A: Policy and Practice, vol. 71,

pp. 96–109, 2015.

[70] N. Makwana, “Disaster and its impact

on mental health: A narrative review,”

Journal of family medicine and primary care,

vol. 8, no. 10, pp. 3090–3095,

Oct 2019. [Online]. Available:

https://pubmed.ncbi.nlm.nih.gov/31742125

[71] S. Hardman, R. Berliner, and G. Tal, “Who will

be the early adopters of automated vehicles?

insights from a survey of electric vehicle

owners in the united states,” Transportation

Research Part D: Transport and Environment,

vol. 71, pp. 248–264, 2019, the roles of users in

low-carbon transport innovations: Electriﬁed,

automated, and shared mobility. [On-

line]. Available: https://www.sciencedirect.

com/science/article/pii/S1361920918303043

[72] S. Yoo, K. W. Koh, and Y. Yoshida, “Are con-

sumers abandoning diesel automobiles be-

cause of contrasting diesel policies? evidence

from the korean automobile market,” Energy

Economics, vol. 92, p. 104969, 2020.

[73] R. Bennett, R. Vijaygopal, and R. Kottasz,

“Willingness of people with mental

health disabilities to travel in driver-

less vehicles,” Journal of Transport &

Health, vol. 12, pp. 1–12, 2019. [On-

line]. Available: https://www.sciencedirect.

com/science/article/pii/S2214140518304080

[74] R. W. Novaco, D. Stokols, J. Campbell,

and J. Stokols, “Transportation, stress, and

community psychology,” American Journal

of Community Psychology, vol. 7, no. 4,

pp. 361–380, Aug 1979. [Online]. Available:

https://doi.org/10.1007/BF00894380

[75] Z. Rezvani, J. Jansson, and J. Bodin,

“Advances in consumer electric vehicle

adoption research: A review and re-

search agenda,” Transportation Research

Part D: Transport and Environment,

vol. 34, pp. 122–136, 2015. [Online].

Available: https://www.sciencedirect.com/

science/article/pii/S1361920914001515

[76] T. A. Novack, D. Labbe, M. Grote, N. Carlson,

M. Sherer, J. C. Arango-Lasprilla, T. Bushnik,

D. Cifu, J. M. Powell, D. Ripley, and R. T.

Seel, “Return to driving within 5 years

of moderate–severe traumatic brain injury,”

Brain Injury, vol. 24, no. 3, pp. 464–471,

2010, pMID: 20184403. [Online]. Available:

https://doi.org/10.3109/02699051003601713

[77] S. Kenyon, G. Lyons, and J. Raf-

ferty, “Transport and social exclusion:

investigating the possibility of promot-

ing inclusion through virtual mobility,”

Journal of Transport Geography, vol. 10,

no. 3, pp. 207–219, 2002. [Online].

Available: https://www.sciencedirect.com/

science/article/pii/S0966692302000121

[78] M. Kamargianni, M. Ben-Akiva, and

A. Polydoropoulou, “Incorporating social

interaction into hybrid choice models,”

Transportation, vol. 41, no. 6, pp.

1263–1285, Nov 2014. [Online]. Available:

https://doi.org/10.1007/s11116-014-9550-5

[79] F. Manca, A. Sivakumar, N. Daina,

J. Axsen, and J. W. Polak, “Modelling

the inﬂuence of peers’ attitudes on

choice behaviour: Theory and empirical

application on electric vehicle preferences,”

Transportation Research Part A: Policy and

Practice, vol. 140, pp. 278–298, 2020. [On-

line]. Available: https://www.sciencedirect.

com/science/article/pii/S0965856420306984

A A: A T  D

Table A1 presents the distribution of the

socioeconomic variables in our sample

and government statistics.

Table A3 shows the average score (when

we ask the respondents to respond on

1-5 point scales) and the proportion of

respondentsâ€™ evaluations of beneﬁts

and concerns regarding FAVs (for multiple

choice questions). We calculate the pro-

portions as follows: the number of peo-

ple who choose the option/sample size

(N=100,803).

A B: R C

Although we use subsample analysis,

we conduct additional analyses by using

the interaction terms on the following

variables: social support, risk preferences,

accident and malfunction. The results are

shown in Tables A4 and A5. We ﬁnd

that the estimation results do not change,

quantitatively and qualitatively, from our

main results, suggesting that our results

in Table IV and Vare robust.

TABLE A1: Socioeconomic Distribution of the Respondents and the Japanese Population

Sample (%) Government Statistics (%)

(n =100,803)

Gender Female 41 51.3

Male 59 48.7

Education level Junior high school or less 2.1 9.5

High school 26.9 42.3

Some college 22.6 15.6

Bachelor / Master / Doctor 45.9 23.9

Other 1.9 8.6

Age 18â€“19 0.2 2.3

20â€“29 5.4 11.7

30â€“39 18.1 13.3

40â€“49 31.9 17.2

50â€“64 25.8 22.1

Over 65 10.7 33.4

Household income <2 million JPY 7.8 18.3

2â€“3 million JPY 8.9 17.2

3â€“4 million JPY 11.9 15.3

4â€“5 million JPY 12.3 12.2

5â€“6 million JPY 11.9 9

6â€“7 million JPY 9.6 6.9

7â€“8 million JPY 9.1 5.8

8â€“9 million JPY 6.9 4.1

9â€“10 million JPY 6.7 3.4

10â€“15 million JPY 10.5 6

15â€“20 million JPY 2.7 1.1

≥20 million JPY 1.7 0.7

Don’t know / don’t want to answer - â€“

Region Hokkaido 4.6 4.2

Tohoku 5.9 6.9

Kanto 38.2 34.4

Chubu 16.6 16.8

Kinki 20.1 17.7

Chugoku 5.1 5.8

Shikoku 2.5 2.9

Kyushu/Okinawa 7.1 11.3

Household size 1 15.6 34.5

2 30.1 27.9

3 23.6 17.6

4 and above 30.1 20

Sources: MIC (2017, 2019a, 2019b)

TABLE A2: Cronbach’s alpha score of each latents

Latent Factors Cronbach’s Alpha

Social Support 0.66

Risk-Averse 0.33

Risk-Seeking 0.29

Accident 0.62

Malfunction 0.65

Burden Reduction 0.55

Safety 0.50

Recycle 0.83

GHG emissions 0.84

Air Pollution 0.87

Biodiversity 0.74

TABLE A3: Proportion and Mean Value of Respondentsâ€™ Evaluations for Latent Construct. The mean value of

the latent construct for the whole population would be zero, with a standard deviation of 1.

Without Disaster Disaster Victims

Mean Std.dev Mean Std.dev

Social Support -0.200 0.761 0.093 0.819

Risk-Averse 0.110 0.516 -0.520 0.517

Risk-Seeking -0.140 0.429 0.660 0.491

Accident -0.007 0.801 0.030 0.815

Malfunction -0.007 0.784 0.030 0.797

Burden Reduction -0.007 0.717 0.030 0.756

Safety -0.007 0.662 0.030 0.681

Recycle -0.017 0.954 0.080 0.899

GHG emissions -0.017 0.966 0.080 0.911

Air Pollution -0.018 0.948 0.080 0.903

Conservation -0.017 0.914 0.080 0.868

TABLE A4: Main Estimation Result with Interactions

Model (1) Model (2)

Before Accident After Accident

Coeﬃcient Std. Err Coeﬃcient Std. Err

Panel (A): Latents

Panel (A-1): Social Support

Social Support 0.100*** 0.013 0.109*** 0.013

Social Support (×Disaster Victims) 0.084*** 0.029 0.068** 0.030

Panel (A-2): Risk, Accident and Merits

Risk-Averse -0.221*** 0.023 -0.181*** 0.023

Risk-Averse (×Disaster Victims) 0.094* 0.053 0.068 0.055

Risk-Seeking 0.093*** 0.027 0.086*** 0.028

Risk-Seeking (×Disaster Victims) 0.161*** 0.058 0.134** 0.061

Accident 0.011 0.055 0.026 0.057

Accident (×Disaster Victims) -0.544*** 0.129 -0.308** 0.136

Malfunction 0.024 0.056 -0.008 0.058

Malfunction (×Disaster Victims) 0.475*** 0.132 0.235* 0.139

Safety -0.008 0.023 0.072*** 0.024

Burden Reduction 0.363*** 0.026 0.288*** 0.027

Panel (A-3): Environmental Perceptions

Recycle 0.018 0.019 0.009 0.020

GHG Emissions -0.061*** 0.016 -0.035** 0.017

Air Pollution -0.092*** 0.015 -0.072*** 0.016

Conservation 0.045** 0.018 0.041** 0.019

Panel (B): Socio-Economic Variables

ln Perceived Value 0.309*** 0.045 0.316*** 0.051

Female -0.259*** 0.024 -0.128*** 0.024

ln Age -0.420*** 0.042 -0.276*** 0.044

ln Income 0.146*** 0.014 0.104*** 0.014

Disaster Experience 0.192*** 0.024 0.185*** 0.025

Commuter 0.048** 0.022 0.038* 0.023

Car Ownership 0.217*** 0.024 -0.108*** 0.025

Accident Awareness -0.392*** 0.020 -0.284*** 0.020

License 0.588*** 0.036 0.232*** 0.036

Panel (C): Others

Heterogeneity Coeﬃcient 0.295*** 0.071 0.457*** 0.095

Constant -0.578** 0.278 0.094 0.303

Log-Likelihood -40,467.62 -38,410.474

Note: Standard errors in parentheses. * p<0.1, ** p<0.05, *** p<0.01.

N=69,391

Prefecture Fixed Eﬀects are Included.

TABLE A5: Substitution Patterns with Interactions

Coeﬃcient Std. Err

Panel (A): Latents

Panel (A-1): Social Support and Risk

Social Support 0.049*** 0.012

Social Support (×Disaster Victims) -0.047* 0.028

Panel (A-2): Risk, Accident and Merits

Risk-Averse -0.014 0.022

Risk-Averse (×Disaster Victims) -0.070 0.052

Risk-Seeking -0.028 0.027

Risk-Seeking (×Disaster Victims) 0.099* 0.057

Accident -0.447*** 0.056

Accident (×Disaster Victims) -0.098 0.133

Malfunction -0.210*** 0.057

Malfunction (×Disaster Victims) 0.078 0.130

Safety 0.081*** 0.023

Burden Reduction -0.108*** 0.027

Panel (A-3): Environmental Perceptions

Waste Reduction 0.049*** 0.018

GHG Emissions 0.063*** 0.020

Air Pollution -0.008 0.178

Conservation -0.094*** 0.190

Panel (B): Socio-Economic Variables

ln Perceived Value Gap 0.014 0.026

Female 0.305*** 0.023

ln Age 0.134*** 0.041

ln Income -0.073*** 0.013

Disaster Experience 0.007 0.023

Commuter -0.090*** 0.021

Car Ownership -0.312*** 0.023

Accident Awareness 0.407*** 0.019

License -0.262*** 0.033

Panel (C): Others

Heterogeneity Construct 0.123*** 0.032

Constant -0.173 0.194

Log-Likelihood -40,809.132

Note: Standard errors in parentheses. * p<0.1, ** p<0.05, *** p<0.01.

N=69,391

Prefecture Fixed Eﬀects are Included.

ResearchGate has not been able to resolve any citations for this publication.

Sharing experience by residents of past disaster sites: Effects of time from disasters and types of experience

Article

Full-text available

Jun 2021

Takaaki Kato

Sharing past disaster experience among the community is an important method of disaster risk reduction. This study distinguishes three groups of residents in disaster-affected areas: the direct experience group, the same generation group that has overlapping ages with the first group but does not have direct experience of the local disaster, and the next generation group. Similarities and differences in the roles of these groups to share their disaster experience was not well documented. This study compares knowledge levels regarding local disasters, content of experience to be shared, and methods of sharing across these groups in Kobe, Japan, where a large earthquake occurred in 1995 and in Sanriku, Japan, where a large earthquake and tsunami hit in 2011. Compared to the other groups, the direct experience group had better knowledge of the disaster and a stronger sense of responsibility to pass down disaster experience themselves. By nine years after the disaster, the members who had shared their experience among this group reached the maximum of 75%. Informal settings like family gatherings are main channel of experience sharing. Having more knowledge on the disaster is a positive factor in an experience sharing behavior. Those who had lived outside of the disaster area reported greater knowledge levels than the others within the direct experience groups, whereas the opposite occurred among the next generation group. A lifecycle model of disaster experience sharing by the three groups is proposed to facilitate policy support for dissimilating disaster experience among communities.

Social Media Analytics as an Enabler for External Search and Open Foresight—The Case of Tesla's Autopilot and Regulatory Scrutiny of Autonomous Driving

Article

Full-text available

May 2021

External search for knowledge and foresight have become strategically important activities for firms in an increasingly uncertain and complex business environment. Novel methods to monitor development are therefore essential for both firms and scholars. This article illustrates how firms can apply one such novel method called Social Media Analytics, a multiplatform approach incorporating multiple external sources drawn from Web 2.0, that enable external search for knowledge but simultaneously avoid information overload. To illustrate the potential of the method, this article draws upon a dataset spanning 36 months from August 2016 to August 2019 and 100 283 publicly posted user-generated contents concerning Tesla to analyze their autopilot and the controversies surrounding autonomous driving. The results show that indications of the regulatory scrutiny Tesla's driverless technology faced in 2019 could be seen in the data across several platforms at an early point and that these signals became stronger over time, especially on blogs and Facebook which exhibited strong indications of future regulatory scrutiny in contrast to Twitter and Instagram. Our results underscore the potential of the Social Media Analytics for external search for knowledge and open foresight that enable firms to tune in to weak signals and scan the periphery.

Safety analysis of freeway on-ramp merging with the presence of autonomous vehicles

Article

Full-text available

Mar 2021
ACCIDENT ANAL PREV

Freeway on-ramp merging areas are high-risk areas for motor vehicle crashes and conflicts due to the variety of driving styles, the difference in mainline and ramp traffic states, and factors related to roadway design and traffic control. The emerging Autonomous Vehicle (AV) technologies are expected to bring substantial improvements in ramp merging operations in general, including the possibility to reduce traffic conflicts and crashes by partially or fully eliminating the critical factors related to the human drivers. In order to investigate the potential safety impacts of AVs at on-ramp merging, this study first proposes a novel conflict index in theory as a specific indicator for ramp merging safety. Then, a merging conflict model is introduced to estimate the index value in various cases by considering the interactions between the mainline and ramp vehicles. In order to account for real-world uncertainties and variations in various crash-contributing factors, the proposed approach incorporates Monte-Carlo method and probabilistic distributions calibrated on the empirical freeway data. The developed approach is later applied in a case study with incremental AV market penetration rates to investigate AV safety impacts at on-ramp merging. The results show clear benefits of AVs in reducing the frequency and severity of the critical merging events. In addition, a sensitivity analysis on essential model parameters shows that the merging safety of AVs is closely related to their gap acceptance policy and the proper functioning of the driving systems, providing further insights into the future development of AVs.

Are Consumers Abandoning Diesel Automobiles because of Contrasting Diesel Policies? Evidence from the Korean Automobile Market

Article

Full-text available

Oct 2020
ENERG ECON

We investigate whether the contrasting set of transportation policies in Korea-reductions in fuel taxes and increases in diesel automobile prices-has decreased emissions. Using a random-coefficient discrete choice model and hypothetical policy sets, we estimate the automobile demand of consumers, the market share of cars by fuel type, and total emissions, assuming that consumer preferences for driving costs change over time. Then, we separately analyze the effect of each policy set on automobile sales and emissions, particularly carbon dioxide, nitrogen oxide, and particulate matter. Our analyses reveal that Korean consumers have become more sensitive toward fuel costs over time and that the emission consequences of Korean policies depend on consumer preferences.

Modelling the influence of peers' attitudes on choice behaviour: Theory and empirical application on electric vehicle preferences

Article

Full-text available

Oct 2020
TRANSPORT RES A-POL

While the importance of social influence on transport-related choices is commonly acknowledged within the transport and travel behaviour research community, there remain several challenges in modelling influence in practice. This paper proposes a new analytical approach to measure the effects of attitudes of peers on the decision making process of the individual. Indeed, while most of the previous literature focused its attention on capturing conformity to a certain real or hypothetical choice, we investigate the subtle effect of attitudes that underlies this choice. Specifically, the suggested measure enables us to model the correlated effect that might indirectly affect the individual's choice within a social group. It combines detailed information on the attitudes in the individual's social network and the social proximity of the individuals in the social network. To understand its behavioural implications on the individual's choice, the in-dividual's peer attitude variable is tested in different components of a hybrid choice model. Our results show that the inclusion of this variable indirectly affects the decision making process of the individual as the peers' attitudes are significantly related to the latent attitude of the individual. On the other hand, it does not seem to directly affect the utility of an alternative as a source of systematic heterogeneity nor does it work as a manifestation of the latent variable, i.e. as an indicator.

Transportation impact assessment following a potential Alpine fault earthquake in New Zealand

Article

Full-text available

Oct 2020
TRANSPORT RES D-TR E

The Alpine fault, one of the major fault systems in New Zealand, extends over 600 km in the South Island. There is a high probability of a rupture in the next 50 years, meaning that the next large earthquake on the Alpine Fault is likely to occur within our, or our children’s, lifetime. This is predicted to result in severe damage to the built environment, especially to infrastructure. To estimate the performance of the road network impacted by an Alpine Fault Magnitude 8 earthquake scenario, this research developed a generalizable methodology to simulate post-disaster transportation impacts on a large regional road network. This included the base model development and model calibration, as well as validation in a post-disaster situation. Post-disaster corridor and district trip analyses were undertaken using the outputs of the dynamic assignment, including mean travel time, total travel time, total travelled distance, and flow. The result of corridor analysis shows the significant increase in flow and total travel time on SH1 between Marlborough and SH7, and SH6 connecting Nelson to Marlborough one day and one week after the impact. The trip analysis of one day, one week, six months, and beyond six months after the earthquake indicates that around 2.02%, 1.16%, 0.39%, and 0.13% of total trips, respectively, cannot occur due to accessibility issues. Almost all of the inter-district trips from the three main impacted districts, namely Buller, Westland, and Grey, would be cancelled for at least one week after the earthquake with the impact on operational performance still ongoing. Trips that can occur typically face a significant increase in travelled distance and, consequently, travel time. The outputs from this model will provide emergency response and transportation organisations with critical information regarding the performance of the network following an Alpine Fault Magnitude 8 earthquake.

Beyond technology adoption: Examining home energy management systems, energy burdens and climate change perceptions during COVID-19 pandemic

Article

Jul 2021
RENEW SUST ENERG REV

The COVID-19 pandemic has rapidly changed our lives. While the global impacts of the pandemic are shocking, the implications for energy burdens, climate policy, and energy efficiency are salient. This study examines income differences in the acceptance of and willingness to pay for home energy management systems during the COVID-19 pandemic among 632 residents in New York. Additionally, this study examines energy profiles, energy burdens, climate change issues, risk perceptions, and social-psychological factors. Compared with low-income households, our findings suggest that high-income households use more energy, have higher utility bills during quarantine mandates, perceive a higher risk of COVID-19 infection, and perceive climate change issues to be better than before. Low-income households, however, experience the highest energy burdens. Regarding HEMS acceptance, high-income households are more willing to adopt energy and well-being-promoting features of HEMS and more willing to pay a higher monthly fee for all the features than other income groups. Overall, participants were more willing to pay a higher price for the energy features than the well-being-promoting features. Low-income households indicate lower social norms, personal norms, and perceived behavioral control over adopting HEMS; they also perceive HEMS to be more difficult to use and less useful. Higher-income households express a higher trust in utilities than low-income households. Surprisingly, cost concerns, technology anxiety, and cybersecurity concerns relating to HEMS do not differ across income groups. This paper addresses the interactions among technology attributes and social-psychological and demographic factors, and provides policy implications and insights for future research.

Investigating the safety and operational benefits of mixed traffic environments with different automated vehicle market penetration rates in the proximity of a driveway on an urban arterial

Article

Jan 2021
ACCIDENT ANAL PREV

Traffic congestion is monotonically increasing, especially in large cities, due to rapid urbanization. Traffic congestion not only deteriorates traffic operation and degrades traffic safety, but also imposes costs to the road users. The concerns associated with traffic congestion increase when considering more complicated situations such as unsignalized intersections and driveways at which maneuvers are entirely dependent upon drivers’ judgment. Urban arterials are characterized by closely spaced signalized and unsignalized intersections and high traffic volumes, which make them a priority while analyzing traffic safety and operation. Autonomous Vehicles (AV) provide ample opportunities to overcome the aforementioned challenges. In essence, this study evaluates the impact of various AV Market Penetration Rates (MPR) on the safety and operation of urban arterials in proximity of a driveway under different traffic levels of service (LOS). Twenty-four separate scenarios were developed using VISSIM, considering six AV MPRs of 0%, 10%, 25%, 50%, 75%, and 100%, and four LOS including A, B, C, and D. Various operational and safety measures were analyzed including traffic density, traffic speed, traffic conflict (rear-end and lane-changing), and driving volatility. The trajectory and lane-based analysis of the traffic density indicates that MPR significantly improves the overall traffic density for all the scenarios, especially under high traffic LOS. Additionally, by increasing the MPR and decreasing the traffic volume of the network, the mean speed increases significantly by up to 6%. Exploring the safety of the scenarios indicates that by increasing the MPR from 0% to 100% for all the LOS, the number of rear-end conflicts and lane-changing conflicts decreases 84%-100% and 42%-100%, respectively. Moreover, assessing the longitudinal driving volatility measures, which represent risky driving behaviors, showed that higher MPRs significantly reduce some of the driving volatility measures and enhance safety.

Air quality and greenhouse gas implications of autonomous vehicles in Vancouver, Canada

Article

Jan 2021
TRANSPORT RES D-TR E

This study explores vehicle fleet emissions changes due to connected and autonomous vehicle (CAV) diffusion, over the years 2030 and 2040 in Metro Vancouver, using the US Environmental Protection Agency’s MOtor Vehicle Emission Simulator (MOVES). Impacts were assessed across scenarios with varying future vehicle kilometers traveled (VKT), transit use, fuel-type, and diffusion rate. In all models, greenhouse gas emissions (GHGs) were reduced due to increasing electric vehicles, though reductions varied. At best, CAVs decreased GHGs by 20% compared to no-CAV conditions in 2040. At worst, we model a 6% decrease in GHGs if autonomy provokes increased use of personal vehicles (Motor City scenario), even with 85% electric vehicles. An overall reduction in emissions is seen for other pollutants, with the exception of PM, ranging from emissions reductions of up to 20% (PM exhaust) to an increase in emissions by 30% (PM2.5 brakewear) in 2040. Increased VKT per CAV had the most significant impact.

Fear and avoidance of driving among drivers involved in a road traffic crash. The role of traumatic fear and driving cognitions

Article

Oct 2020
TRANSPORT RES F-TRAF

Cornelia Mairean

Fear and avoidance of driving are possible consequences of involvement in road traffic crashes (RTCs). Few studies have assessed the factors associated with fear and avoidance of driving after an RTC. The aim of this present study is to investigate the relations among trauma appraisals of fear, negative driving cognitions, fear and avoidance of driving in a sample of people who experienced vehicle crashes. Further, the mediating role of negative driving cognitions in the relation between trauma appraisals, fear of driving and avoidance of driving was assessed. The sample was comprised of 116 drivers who had been involved in an RTC in the past two years. Negative driving cognitions positively predicted fear of driving and avoidance of driving. Moreover, negative driving cognitions mediated the relation among trauma appraisals of fear with fear of driving and avoidance of driving. The theoretical and practical implications of these results are discussed.

Trust More, Fear Less: The Role of Social Support in Fully Automated Vehicle Choice

Abstract

Recommended publications

Willingness to Buy and/or Pay Disparity: Evidence from Fully Autonomous Vehicles

To Fully Automate or Not? Investigating demands and Willingness to Pay for Autonomous Vehicles based...

Buy and/or Pay Disparity: Evidence from Fully Autonomous Vehicles

Perceptions and Attitudes Towards the Deployment of Autonomous and Connected Vehicles: Insights from...