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Stated ownership and intended in-vehicle time use of privately-owned autonomous vehicles
Chapter 29
Stated ownership and intended in-vehicle time use of privately-owned autonomous vehicles Ying Jianga, Junyi Zhangb, Yinhai Wangc a
Departments of Health Sciences, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan; bGraduate School for International Development and Cooperation, Hiroshima University, Higashi-Hiroshima, Japan; cDepartment of Civil Engineering, University of Washington, Seattle, WA, USA
Chapter outline 1. Introduction 2. Revealed and stated preference survey 2.1 Efforts to enhance the reliability of SP responses 2.2 SP attributes 2.3 SP responses 2.4 Other survey contents 3. Ownership analysis of privatelyowned autonomous vehicles 3.1 Model results and discussion 3.1.1 Random effects of WTP and SP attributes 3.1.2 Other factors 4. Analysis of intended time use inside privately-owned autonomous vehicles
577 579 579 580 580 581 581 581 584 584
4.1 Generalized structural equation model (GSEM) 4.2 Modeling results and discussion 5. Conclusion 5.1 Findings about ownership of privately-owned autonomous vehicles 5.2 Findings about time use inside privately-owned autonomous vehicles 5.3 Future research issues Acknowledgments References
585 587 590
591
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1. Introduction The development of autonomous vehicles (AVs) will provide people with more mobility options. Research on ownership and usage of AVs has therefore Mapping the Travel Behavior Genome. https://doi.org/10.1016/B978-0-12-817340-4.00029-2 Copyright © 2020 Elsevier Inc. All rights reserved.
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become a hot topic in many countries. Accumulation of more scientific evidence is important for the future deployment of AVs. As stated by Zhang et al. (2018), most existing studies analyze the impacts of shared AVs (SAVs) on vehicle ownership; however, limited efforts have been made to understand the impacts of privately-owned AVs (PAVs). Using the 2011 Atlanta Travel Survey data to conduct a simulation analysis, Zhang et al. (2018) found that AVs could allow nearly 20% of households to reduce their owned vehicles while maintaining the current travel patterns, by assuming a 100% AV penetration rate and households’ homogeneous preferences for AVs. Unfortunately, households’ decision-making mechanisms on PAVs ownership have been neglected until now. There are other similar simulation-based studies; however, these studies did not collect data on users’ preferences for autonomous vehicles. Acheampong and Cugurullo (2019) applied socio-psychological and socioecological theories to capture factors affecting the adoption of AVs, and Pettigrew et al. (2019) compared purchase/use intentions of owning a PAV or using an SAV; however, the influence of AV attributes was not reflected in these two studies. Gkartzonikas and Gkritza (2019) made a timely review of existing studies on AVs, in terms of attitude, motivation, perception, belief and confidence, expectation, willingness-to-pay, intention, use of AV as egress transport mode, use of automated features in vehicle, acceptance, adoption, and ownership, etc. Shabanpour et al. (2018) reported that more productive use of in-vehicle time is one of the most common expected benefits of using an AV; however, they did not investigate how use of an AV affects in-vehicle time use. Instead, Shabanpour et al. (2018) implemented a best-worst choice experiment, which used the following attributes to define AV alternatives and asked respondents to make a choice: purchase price, fuel cost to drive, driving range on one tank, overall safety level, emission rate, driver liability, and exclusive lane. To date, no study on time use inside AVs can be found in the literature. PAVs, the target of this study, will dramatically reduce and eventually eliminate human interventions in driving tasks, which will allow human beings to use the time inside PAVs in a more efficient way. In the case of conventional vehicles, drivers usually experience more negative feelings inside vehicles than positive feelings, because they have to operate the vehicle during the whole course of driving. In the case of PAVs, drivers can use more time on non-driving tasks, which are expected to bring more positive feelings to them. However, little is known about such time use from existing studies. To fill the above research gap, a revealed preference (RP) and stated preference (SP) survey was implemented on the Internet to 1002 residents in Japan in 2016. In the RP part, respondents reported their actual time use inside their currently-owned vehicles for both short- and long-distance trips. In the SP part,
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respondents reported their intended in-vehicle time use by referring to the RP time use. Such a two-step survey allows respondents to provide reliable answers. Furthermore, the SP part contains two groups of questions: the first group is about stated choices of four types of vehicles (i.e., conventional vehicle (hereafter, CV), PAV with conditional automation (Conditional PAV), PAV with high automation (High PAV), and PAV with full automation (Full PAV)) under different future scenarios of PAV attributes, and the second group is about the intended time use inside PAVs. Only respondents choosing a PAV were asked to report their intended invehicle time use. In this study, only data from car users (740) were used. For the conventional vehicle, or CV, car users were asked to refer to their currently owned vehicle. In the remaining part of this chapter, the above survey is first introduced. After that, ownership of PAVs is analyzed based on a panel mixed logit model. Next, the intended inside-PAV time use is represented using a multilevel simultaneous-equation negative binomial regression model, and its influential factors are estimated. Finally, this study is concluded, together with a discussion of future research issues.
2. Revealed and stated preference survey For this study, the above RP-SP survey was conducted in September 2016 for both car users and non-car users living in different parts of Japan. As a result, 1002 respondents provided valid data, whose distributions of age and gender were controlled to be the same as those in the populations by region. Here, only data from car users (740) were used.
2.1 Efforts to enhance the reliability of SP responses Before answering the SP questions, each respondent was first asked to report his/her familiarity with autonomous vehicles. It was found that more than 90% of the respondents knew autonomous vehicles to some extent. Second, the driving automation functions of the three types of AVs (Conditional AV, High AV, Full AV) were explained to respondents, as compared with conventional vehicles, based on the text messages from SAE International (2014). Third, respondents were shown a news report dated August 25, 2016, about the release of the first Japanese AV, a minivan “Serena” with ProPilot (less advanced than Conditional AVs), which allows the vehicle to run automatically under a single-lane traffic environment on expressways (in fact, even before 2016, major mass media had already broadcast various news reports about AVs). Fourth, the main advantages and disadvantages of autonomous vehicles were briefly presented to respondents. Fifth, respondents were asked to compare the three types of PAVs (Conditional PAV, High PAV, Full PAV) with their currently owned vehicles (i.e., the choice set contains four
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alternatives) for answering their ownership and usage. They were further asked to answer SP questions by assuming that the current trend of personal income change would continue in the future. As for SP attributes, penetration rates and additional costs of the three types of PAVs, insurance discount rates for PAVs (two attributes: one for Conditional/High PAV and the other for Full PAV), parking cost and timing for the release of PAVs onto the market. The meanings of each SP attribute were explained to respondents before they answered SP questions. With the above survey efforts, respondents were expected to have a better understanding about autonomous vehicles.
2.2 SP attributes Penetration rates were introduced to reflect the potential influence of social interactions on respondents’ choices of the three types of PAVs. Additional costs were determined by referring to existing studies in both Japan and other countries. The additional costs and discount rates of insurance for Full PAV were set to be higher than those of the two other PAVs. Because PAVs can park automatically with the assistance of self-driving and self-parking functions, two levels of parking cost reduction were set. Additional costs, insurance discount rates and permanent parking costs were given based on respondents’ currently-owned vehicles. Finally, the release timing of PAVs to the future market was also assumed. (For details of the SP attributes, refer to Jiang et al., 2018).
2.3 SP responses With the above SP attributes, an orthogonal fractional factorial design was employed and 18 SP profiles were finally obtained. In the survey, the 18 profiles were randomly grouped into six blocks and each respondent was randomly assigned with one block. In other words, each respondent was requested to choose one out of Conditional PAV, High PAV, Full PAV, and the currently-owned vehicle (CV) three times, for short- and/or long-distance driving trips (i.e., vehicle ownership). After answering the three SP profiles, each respondent further reported his/her intended time use inside Conditional/High PAV and/or Full PAV (i.e., vehicle usage), respectively, if a PAV was selected for short- and/or long-distance trips. Items of time use inside PAVs (i.e., multitasking during travel) included monitoring of the movement of car and its surrounding traffic, e-mailing, document-making, reading books/news and comic books, watching TV, enjoying games and Internet surfing, talking (via mobile phone, SNS, or with passengers), sleeping, scenery watching and music listening, smoking, eating, putting on make-up, shaving and so on.
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2.4 Other survey contents In addition to the above SP contents, respondents were also asked to report their willingness to pay (WTP) for Conditional PAV, High PAV, and Full PAV, after giving information about their recent income changes. Furthermore, respondents reported their actual travel behavior and driving experience (unsafe driving incidents during driving, affective feeling, etc.) for short- and/or longdistance driving trips, self-cognition of behavioral changes toward safe driving (i.e., whether and how much he/she wants to improve his/her current driving safety level). Finally, individual attributes were investigated.
3. Ownership analysis of privately-owned autonomous vehicles Among the 740 car users, 576 provided 1728 valid SP responses. As for additional costs when purchasing an AV, respondents were willing to pay 402,233 JPY (i.e., WTP) for a Conditional PAV, on average (22.3% higher than the original price of conventional car), 563,847 JPY (31.9% increase) for a High PAV, and 793,611 JPY (45.7% increase) for a Full PAV, respectively. Excluding zero payments, it is revealed that the WTP values increase to an average of 499,324e934,518 JPY. In the case of the aforementioned Serena with partial driving automation, additional purchasing cost in the Japanese market is currently 243,000 JPY.1 Compared to this value, it can be said that the above WTP values from the survey are reliable. Because each respondent provided multiple SP choices, these choice answers may be correlated. To address such correlations, a panel mixed logit model (McFadden and Train, 2000; Train, 2009; Hole, 2007, 2013) is employed (hereafter, PMXL model). Here, three SP responses reported by each respondent were treated as a panel data.
3.1 Model results and discussion To estimate the PMXL model, the conventional vehicle was selected as a reference. For explanatory variables, WTP for additional purchase cost reported by respondents and the five SP attributes for different types of PAVs are introduced with random-effects, with parameters assumed to be invariant across the three types of PAVs. Driving experience, behavioral change toward safe driving, future income expectation, and individual attributes are further introduced to explain the choices of the three types of PAVs, where their parameters are assumed to be invariant across the three PAVs. STATA software (Version 15.0) was applied to estimate the PMXL model, with 500 Halton draws and 50 burnings. Table 29.1 shows the estimation results, which confirm
1. http://www.nissan.co.jp/SP/SERENA/VLP/OPTION/option_all.html.
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TABLE 29.1 Estimation results of the PAVs ownership model. Parameter
tScore
Sig.a
Mean
0.05
4.98
***
Stdevb
0.16
5.45
***
Interval valuec
[0.26, 0.36]
Mean
0.13
5.82
***
Stdev
0.13
5.93
***
Interval valuec
[0.38, 0.12]
Mean
0.08
2.42
**
Stdev
0.23
4.85
***
Interval valuec
[0.53, 0.37]
Mean
0.02
1.69
*
Stdev
0.03
1.42
Interval valuec
[0.08, 0.04]
Mean
0.08
2.36
**
Stdev
0.27
4.83
***
Interval valuec
[0.61, 0.45]
Mean
0.06
0.47
***
Stdevb
3.60
4.95
Interval valuec
[7.00, 7.12]
Explanatory variables Attributes with random effects WTP for additional purchase cost (10,000 JPY)
SP attributes
Additional purchase cost of PAV (10,000 JPY)
Insurance reduction rated of PAV
Permanent parking cost reduction rated of PAV
Penetration rated of PAV
Release timing of PAV to the market (years)
b
b
b
b
Individual attributes 15e29 years old [aged under 30s] (Yes: 1; No: 0)
7.19
3.29
***
40e49 years old [aged 40s] (Yes: 1; No: 0)
3.18
1.80
*
50e59 years old [aged 50s] (Yes: 1; No: 0)
1.93
0.98
*
60e69 years old [aged 60s] (Yes: 1; No: 0)
4.30
1.87
Gender (Male: 1; Female: 0)
2.08
1.49
High-education (University level or above: 1; otherwise: 0)
3.82
2.72
***
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TABLE 29.1 Estimation results of the PAVs ownership model.dcont’d Parameter
tScore
Sig.a
Number of elderly household members (aged 65þ)
2.76
2.98
***
Number of primary & secondary school students in household
2.88
2.53
**
2.31
1.99
**
exp (absolute value of income decrease)
9.75
3.33
***
ln (absolute value of income increase þ 1)
0.90
0.12
Short-distance driving experience
Sudden braking/handling (Yes: 1; No: 0)
5.71
2.77
***
Driving time per trip (min)
0.11
3.78
***
Driving frequency (times/week)
0.61
1.42
Driving purpose 1: commuting purpose (Yes: 1; No: 0)
0.98
0.42
Driving purpose 2: shopping purpose (Yes: 1; No: 0)
0.59
0.40
Sudden braking/handling (Yes: 1; No: 0)
14.65
4.43
Driving time per trip (min)
0.002
0.48
Driving frequency (times/week)
0.92
0.59
Driving purpose 1: tourism (Yes: 1; No: 0)
2.50
1.76
Driving purpose 2: going back to hometown (Yes: 1; No: 0)
2.84
1.37
Explanatory variables
Behavioral change toward safe driving Stage of driving safety improvement (Try to improve: 1; otherwise: 0) Future expectation of income
Long-distance driving experience
Initial log-likelihood
2395.52
Converged log-likelihood
1309.14
McFadden Rho-squared
0.454
Adjusted McFadden Rho-squared
0.440
a
Significant level: *10%; **5%; ***1%). Standard deviation. Interval estimate of parameter under 95% confident level. d The rate value is the original ratio (%) multiplied by 100 (e.g., 50% is transformed to 50). b c
***
*
584 PART | II New research methods and findings
that the model accuracy is sufficiently high, because the McFadden Rho-squared value is 0.454 (the adjusted value is 0.440).
3.1.1 Random effects of WTP and SP attributes Here, a normal distribution is assumed to capture the random effects of WTP and SP attributes. This is because different respondents may show different responses to WTP and SP attributes and positive and negative responses may co-exist among individuals. Such random effects are also a reflection of unobserved heterogeneities. In Table 29.1, interval estimates under 95% confidence level for random-effect variables are also shown. It is observed that four negative mean parameters and two positive mean parameters are obtained; however, interval values of these six parameters cover both negative and positive values. All these random-effect parameters (mean and/or standard deviation parameters) are statistically significant. Looking at the mean parameters, only WTP and the SP attribute “additional purchase cost of PAV” show intuitive results, i.e., the higher the WTP and the cheaper the additional purchase cost of PAV, the higher the probability of choosing an PAV. However, the other unintuitive results do not necessarily imply that relevant estimations are wrong. The standard deviation parameter of “permanent parking cost reduction rate of PAV” is not significant, suggesting that variations of responses to this variable across individuals are not remarkably large, i.e., individuals have similar concerns about permanent parking cost. On the other hand, the mean parameter of “release timing of PAV to the market” is insignificant, but its standard deviation is significant, meaning that even though release timing does not matter to decisions on PAVs ownership on average, this is not true to every individual. Notably, the standard deviation parameter of “release timing of PAV to the market” is the largest among the six randomeffect variables, which is 13e28 times higher than other significant standard deviations. The above results indicate that unobserved heterogeneities play a complicated role in explaining PAV ownership. In other words, heterogeneous preferences of different population groups should be taken into consideration when deploying autonomous vehicles in the market. 3.1.2 Other factors The significant and positive parameter of behavioral change toward safe driving indicates that the attitude of trying to improve current driving safety level is associated with respondents’ higher preferences for autonomous vehicles. Such drivers may find it difficult to improve driving safety by themselves and consequently tend to rely on advanced technologies. As for driving experience, sudden braking/handling experiences in short- and long-distance driving trips play opposite roles in decisions on PAVs ownership. Shortdistance experience discourages the ownership, while long-distance experience encourages the ownership. This may be because short-distance driving
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usually involves more complicated traffic situations and is consequently riskier than long-distance driving, while long-distance driving usually takes place on expressways, which may be less risky than ordinary roads but drivers may be more likely to experience drowsy driving. In addition, drivers are sensitive to driving time in short-distance trips but are insensitive in long-distance trips. As for trip purposes, only the tourism purpose affects PAVs ownership: respondents prefer using a PAV for tourism trips. Driving frequency does not seem to affect people’s preferences for ownership. According to the prospect theory (Tversky and Kahneman, 1992), people are more sensitive to loss than to gain. In line with such a consideration, expected income decrease is transformed into an exponential form and expected income increase into a logarithmic form. The modeling results show that decrease in future income matters to the PAV ownership decision, but the increase does not. The above model results may be a reflection of income change rates (22.1% for those reporting an increase and 49.1% for those reporting a decrease). In total, there are eight variables for individual attributes (age, gender, education, and household structure), among which six are significant. To capture the non-linear effects of age, it is categorized into five dummy variables, where the variable “aged 30s [30e39 years old]” is set as a reference. Aged under 30 show a negative response to autonomous vehicles and other age groups respond positively. Among these age variables, the magnitude (7.19) of “aged under 30” is the largest, which is 3.7 times larger than the smallest (aged 50s: 1.93), indicating its large influence. Gender does not affect the ownership decision. Respondents with high education level prefer to own a PAV. Household structure variables show opposite influences: the parameter of the number of elderly members is negative and that of the number of school students is positive.
4. Analysis of intended time use inside privately-owned autonomous vehicles 4.1 Generalized structural equation model (GSEM) In total, this study classified the intended time use inside autonomous vehicles into the following seven categories, as shown in Table 29.2, where time use data of short-distance and long-distance trips for all three types of autonomous vehicles are pooled together. The RP data show that actual driving-focused time inside vehicles accounts for 56.6% of the whole travel time in the case of short-distance trips and 61.0% in long-distance trips. In contrast, the SP data reveal that for short-distance trips, respondents intended to spend 22.5% and 11.7% of the whole travel time focusing on driving when using a Conditional/High PAV and a Full PAV, respectively. In the case of long-distance trips, the respective shares of the
586 PART | II New research methods and findings
TABLE 29.2 Summary of intended time use inside autonomous vehicles.
Variable name
Content
Zero observations
Average time with zeros
Average time without zeros
TimeUse_1
Driving-focused time use (to monitor the movement of car and its surrounding traffic)
152 (18.8%)
21 min
26 min
TimeUse_2
Time use on emailing, documentmaking, reading books/news and comic, etc.
348 (43.0%)
11 min
19 min
TimeUse_3
Time use on watching TV, enjoying games and Internet surfing, etc.
492 (60.8%)
9 min
22 min
TimeUse_4
Time use on talking (via mobile phone, SNS, or with passengers)
167 (20.6%)
24 min
30 min
TimeUse_5
Time use on sleeping
552 (68.2%)
12 min
37 min
TimeUse_6
Time use on scenery watching and music listening
251 (31.0%)
17 min
24 min
TimeUse_7
Time use on other purposes (e.g., smoking, eating, making-up, shaving)
431 (53.3%)
11 min
24 min
intended driving-focused time are 27.0% if a Conditional/High PAV is used and 12.9% if a Full PAV is used. To reflect the influence of the many zero observations, a negative binomial regression model is used to represent each time use variable. The above time use variables were reported only with respect to those SP responses of Conditional PAVs, High PAVs, and Full PAVs. As a result, data used for this part of the study contains 378 car user respondents who provided 809 SP responses. Because of the repeated observations from each respondent, the 809 samples are probably not independent of each other. To address such potential correlation, this study further adopts a multilevel modeling approach by adding an individual-specific error component to the above negative binomial regression
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model. Such error component can also be used to incorporate the influence of unobserved individual heterogeneity. With the above considerations and mathematical treatments, this study jointly estimated the above seven time use models by using the GSEM module of Stata (Version 15.0). GSEM is an abbreviation of generalized structural equation model. GSEM is the combination of generalized linear model (GLM) estimation (McCullaugh and Nelder, 1989) and the SEM estimation (Baum, 2016). It can include not only continuous responses, but also binary, ordinal, count, or multinomial responses, and not only linear regression model, but also gamma regression, logit, probit, ordinal logit/probit, Poisson, negative binomial, and so on.
4.2 Modeling results and discussion In total, three categories of explanatory variables (individual attributes, driving preference, and experience of risky driving) were selected in this study (See Table 29.3). To confirm the model performance (see Table 29.4), this study made a Chisquare test between the multilevel model (converged final loglikelihood ¼ 17537.606) and the fix-effects model (converged final loglikelihood ¼ 18133.246) and found that the test statistic is 1191.28 (¼2*(17537.606 (18133.246))), which is larger than the critical value of 20.09 with the degree of freedom being 8. This indicates that the above-built simultaneous-equation time use model is significantly different from the fixedeffects model at 1% level.
TABLE 29.3 Explanatory variables for GSEM. Category
Detailed explanatory variables
Individual attributes
l l l
Age: actual age (years old) Gender: 1. Male, 0. Female Education: 1. university or above, 0. otherwise
Driving preference
Measured in ordinal scale: 1. definitely yes; 2. probably yes; 3. neutral; 4. probably no; 5. definitely no l Like_drive: like to drive l Drive_more: Willing to drive more l Good_driving: good at driving l Danger_driving: driving is dangerous l Avoid_driving: avoid driving as much as possible
Experience of risky driving
l
l
Short_dis_brake: 1. experienced sudden braking/ handling in short-distance driving, 0. Otherwise Long_dis_brake: 1. experienced sudden braking/ handling in long-distance driving, 0. otherwise
588 PART | II New research methods and findings
TABLE 29.4 Modeling estimation results of the multilevel simultaneous-equation negative binomial regression model. TimeUse_1 Explanatory variables
Parameter
TimeUse_2
TimeUse_3
P > jzj
Var ratio
Parameter
P > jzj
Var ratio
Parameter
P > jzj
Var ratio
****
27.5%
Individual attributes Age
0.029
***
27.7%
0.012
**
6.4%
0.007
Gender
0.715
***
17.8%
0.535
***
13.3%
1.060
Education
0.031
0.0%
0.368
*
6.1%
0.136
0.4%
3.4%
0.342
**
48.8%
0.038
0.3%
12%
Driving preference 0.104
Like_drive Drive_more
0.037
Good_driving
0.244
Danger_driving
0.069
Avoid_driving
0.263
0.4%
0.050
0.9%
0.071
1.0%
***
18.5%
0.160
10.7%
0.258
14.7%
1.6%
0.001
0.0%
0.207
***
25.8%
0.093
4.3%
0.379
***
4.4%
0.054
0.5%
0.558
10.1% **
37.6%
7.2%
Driving experience Short_dis_brake
0.397
Long_dis_brake
0.153
**
0.1%
0.056
9.4%
0.676
**
0.1%
4.022
***
Individual-specific error components: Var [ 0.123** Mean
1.000
3.838
Inalpha
0.839
1.012
***
1.738
Note: (1) * significant at 10% level, ** 5% level, *** 1% level. (2) [Time use variables] TimeUse_1: Driving-focused time use (to monitor the movement of car and its surrounding traffic); TimeUse_2: Time use on e-mailing, document-making, reading books/news and comic, etc.; TimeUse_3: Time use on watching TV, enjoying games and Internet surfing, etc.; TimeUse_4: Time use on talking (via mobile phone, SNS, or with passengers); TimeUse_5: Time use on sleeping; TimeUse_6: Time use on scenery watching and music listening; TimeUse_7: Time use on other purposes (e.g., smoking, eating, making-up, shaving). (3) [Individual attributes] Age: actual age (years old); Gender: 1-male, 0-female; Education: 1-university or above, 0-otherwise. (4) [Driving preference] (1: definitely yes, 2: probably yes, 3: neutral, 4: probably no, 5: definitely no) e Like_drive: like to drive; Drive_more: Willing to drive more; Good_driving: good at driving; Danger_driving: think driving is dangerous; Avoid_driving: avoid driving as much as possible. (5) [Experience of risky driving] Short_dis_brake: 1 e experienced sudden braking/handling in short-distance driving, 0 e otherwise; Long_dis_brake: 1 e experienced sudden braking/handling in long-distance driving, 0 e otherwise.
There are 70 parameters for the 10 explanatory variables, of which 29 are statistically significant at either 10%, 5% or 1% level. Concerning individual attributes, in total, 21 relevant parameters were estimated, of which 11 are significant. Older drivers are more likely to focus on driving (TimeUse_1), spending time on e-mailing, document-making, reading books/news and comic, etc. (TimeUse_2), on talking (via mobile phone, SNS, or with passengers) (TimeUse_4), and on scenery watching and music listening (TimeUse_6). Male drivers prefer more time on driving
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Stated ownership and intended in-vehicle time Chapter | 29
TABLE 29.4 Modeling estimation results of the multilevel simultaneous-equation negative binomial regression model. TimeUse_4
TimeUse_5
Parameter
P > jzj
Var ratio
Parameter
0.023
***
P > jzj
TimeUse_6
TimeUse_7
Var ratio
Parameter
P > jzj
Var ratio
Parameter
2.1%
0.030
***
33.0%
0.010
9.4%
0.518
***
10.1%
0.637
P> jzj
Var ratio
***
12.8%
28.7%
0.009
0.211
2.5%
0.576
0.067
0.2%
0.430
5.0%
0.169
1.0%
0.175
0.9%
0.127
8.2%
0.228
13.1%
0.044
0.6%
0.216
13.1%
*
0.047
1.0%
0.182
7.3%
0.178
*
9.2%
0.335
0.152
11.8%
0.296
22.2%
0.334
***
37.5%
0.188
0.008 0.269
***
0.0%
0.049
0.7%
0.041
0.6%
0.078
44.1%
0.289
25.1%
0.123
6.1%
0.262
3.3%
**
9.9% 1.8% **
23.2%
6.6%
0.003
0.0%
0.560
7.0%
0.014
0.0%
0.196
0.307
3.5%
0.668
8.1%
0.272
1.8%
0.570
**
3.921
***
3.438 0.425
***
5.724 2.132
***
2.683 0.857
***
27.4%
1.0%
1.447
(TimeUse_1), on e-mailing, document-making, reading books/news and comic, etc. (TimeUse_2), on watching TV, enjoying games and Internet surfing, etc. (TimeUse_3), on sleeping (TimeUse_5), on scenery watching and music listening (TimeUse_6), and on other purposes (TimeUse_7). A degree of university or above is associated with longer time on e-mailing, documentmaking, reading books/news and comic, etc. (TimeUse_2). As for driving preference, drivers preferring more time on driving (TimeUse_1) are those who think they are not good at driving and do not try to avoid driving as much as possible. Sleeping time is only affected by gender.
590 PART | II New research methods and findings
Drivers who like to drive are likely to spend less time on e-mailing, documentmaking, reading books/news and comic, etc.; however, such driving liking attitudes do not matter to other time use decisions. Drivers who are good at driving prefer less time spent on scenery watching and music listening. The driving preference “driving is dangerous” is not influential to any time use decisions. Drivers who try to avoid driving as much as possible prefer less time on enjoying games and Internet surfing, talking, and other purposes. Regarding experience of risky driving in driver’s daily life, drivers experiencing more sudden braking/handling in short-distance driving prefer more time focusing on driving; however, such experience does not matter for other time use decisions. Experiencing more sudden braking/handling in longdistance driving encourages drivers to spend more time on e-mailing, document-making, reading books/news and comic, watching TV, enjoying games and Internet surfing, and other purposes; however, such experience does not matter to time decisions on driving, talking, sleeping, and scenery watching and music listening. The ratio of total variances explained by each of the explanatory variables further informs us about the size of influence of each variable. Time use focusing on driving is more influenced by age (27.7%), gender (17.8%), and driving preferences of Avoid_driving (25.8) and Good_driving (18.5%), than other variables. Driving preferences of Like_drive (48.8%) and gender (13.3%) are more influential to time use on e-mailing, document-making, reading books/news and comic, etc. Time use on watching TV, enjoying games and Internet surfing, etc. is more associated with driving preference of Avoid_drive (37.6%) and gender (27.5%). Driving preference of Avoid_driving (44.1%) and age (28.7%) are the only influential factors, which account for 72.8% of the total variance. Driving preference of Good_driving (37.5%), age (33.0%) and gender (10.1%) are more associated with time use on scenery watching and music listening. Lastly, time use on other purposes is more influenced by driving preferences of Drive_more (27.4%) and Avoid_drive (23.2%) and gender (12.8%).
5. Conclusion Understanding people’s preferences for owning and using autonomous vehicles in the future is crucial for policy makers; however, our knowledge remains very limited because of insufficient research accumulation. For providing additional insights into existing literature, the present study aimed to reveal unknowns about ownership and usage of autonomous vehicles in the context of Japan, based on an SP survey. Different sets of observed factors were selected for explaining ownership and usage of autonomous vehicles. Unobserved heterogeneities are captured by using a mixed logit model to represent the ownership behavior and by building a multilevel simultaneous-equation negative binomial regression model to describe usage behavior.
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5.1 Findings about ownership of privately-owned autonomous vehicles All WTP and SP attributes consistently showed mixed effects on the ownership of autonomous vehicles, i.e., both negative and positive influences are identified. Such results are not unrealistic, considering the existence of various types of persons. This result implies that careful segmentation strategies are required for making cost-related policies. This result also suggests that noncost factors may be more useful to effectively deploy autonomous vehicles in the real world. People trying to improve their driving safety are more likely to own an autonomous vehicle. Experience of risky driving behavior in shortdistance trips reduces the probability of choosing an autonomous vehicle, while experience of risky driving behavior in long-distance trips plays an opposite role. In the case of long-distance trips, the tourism purpose is associated with a higher ownership level than other purposes. Based on the results, daily-trip purposes do not influence ownership. Increase in future income does not matter to decisions on ownership; however, decrease in future income reduces the preference for PAV ownership. Young people tend to avoid owning an autonomous vehicle, while people aged 40s and 60s are more likely to own it. Gender does not matter to decisions regarding ownership. Other persons showing a higher level of ownership preference are those with a university degree or above, having more primary and secondary school students in households, and having fewer elderly members in households.
5.2 Findings about time use inside privately-owned autonomous vehicles First, regarding driving-focused time use, major factors linked to more time concentrating on driving are being male and older persons: accounting for 45.5% of the total variance of driving-focused time use; and the attitudes of “being bad at driving” and “do not avoid driving as much as possible”, which explain 44.3% of the total variance. Thus, enhancing the awareness of driving safety is helpful for drivers to concentrate more time on driving. Second, the preferred sleeping time was 12 min, on average (including those without sleeping). About 70% of respondents did not prefer sleeping while using an autonomous vehicle. Excluding those non-sleeping samples, the average sleeping time preferred was 378 min. It is controversial whether sleeping inside autonomous vehicles could be recommended or not under the driving mode of full automation. Analyses in this study confirmed that shorter sleeping time is only associated with females, but this only explains 9.4% of total variance. This suggests that it is necessary to make more efforts in the future to figure out more influential factors. Third, with respect to other non-driving tasks, as expected, experience of risky driving in long-distance trips consistently increases time spent on e-mailing, document-making, reading books/
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news and comic, on watching TV, enjoying games and Internet surfing, and on other purposes. Driving automation, which may reduce risky driving, could mitigate negative travel utility in long-distance trips, via more in-vehicle multitasking. Different types of driving preference affect time use on the above non-driving tasks in different ways. Similar differences are also observed with respect to individual attributes.
5.3 Future research issues The findings in this study not only provide unique insights into the deployment of PAVs in terms of policymaking, product design, and marketing, and so on, but also advances time use research associated with travel behavior and PAVs. However, this study revealed mixed effects of PAV-related cost variables. This implies that deployment of autonomous vehicles needs more careful market segmentation strategies; it may also be worth exploring the influence of more non-cost factors. In recent years, mobility as a service (MaaS) has attracted more and more attention, which is defined as “the integration of various forms of transport services into a single mobility service accessible on demand” by the MaaS Alliance.2 Within the context of integrated transport services such as MaaS, people may decide whether to own an autonomous vehicle or not depending on how its usage complements other modes. More relevant research should be conducted in the future, for example, with respect to: shared ownership and usage of autonomous vehicles; as a moving home/office/hotel; combined with various life activities; by considering different needs of different population groups, and so on. The use of autonomous vehicles will make in-vehicle time use more efficient. In this regard, it is worth exploring how to design the inside-vehicle space for facilitating more efficient use of travel time. Efficient use of time inside autonomous vehicles increases the utility of owning an autonomous vehicle; as a result, more cars may be observed on roads, if effective countermeasures are not taken. Shared use of autonomous vehicles and seamless transfer between autonomous vehicles and other travel modes may need to be promoted from a perspective of social desirability.
Acknowledgments This study was fully supported by the Grants-in-Aid for Scientific Research (A), Japan Society for the Promotion of Science (JSPS) for the project titled “Interdisciplinary Research on Policies Promoting Young People’s Migration to and Permanent Residence in Local Cities” (Principal researcher: Junyi Zhang, Hiroshima University; 15H02271).
2. https://maas-alliance.eu/homepage/what-is-maas/.
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References Acheampong, R.A., Cugurullo, F., 2019. Capturing the behavioural determinants behind the adoption of autonomous vehicles: conceptual frameworks and measurement models to predict public transport, sharing and ownership trends of self-driving cars. Transportation Research Part F 62, 349e375. Baum, C.F., 2016. Introduction to GSEM in Stata. ECON 8823: Applied Econometrics; Boston College, Spring 2016. Retrieved from: https://www.researchgate.net/file.PostFile Loader.html? id¼5767646c615e27f5a06a0db9&assetKey¼AS%3A374896539521024%401466393708049. Gkartzonikas, C., Gkritza, K., 2019. What have we learned? A review of stated preference and choice studies on autonomous vehicles. Transportation Research Part C 98, 323e337. Hole, A.R., 2007. Estimating mixed logit models using maximum simulated likelihood. STATA Journal 7 (3), 388e401. Hole, A.R., 2013. Mixed Logit Modelling in Stata e An Overview [PowerPoint Slides]. Retrieved from: http://www.stata.com/meeting/uk13/abstracts/materials/uk13_hole.pdf. Jiang, Y., Zhang, J., Wang, Y., Wang, W., 2018. Capturing ownership behavior of autonomous vehicles in Japan based on a stated preference survey and a mixed logit model with repeated choices. International Journal of Sustainable Transportation. https://doi.org/10.1080/ 15568318.2018.1517841. McCullaugh, P., Nelder, J.A., 1989. Generalized Linear Models In: Monographs on Statistics and Applied Probability, second ed., vol. 37. Chapman and Hall, New York. McFadden, D., Train, K., 2000. Mixed MNL models for discrete response. Journal of Applied Econometrics 15 (5), 447e470. Pettigrew, S., Dana, L.M., Norman, R., 2019. Clusters of potential autonomous vehicles users according to propensity to use individual versus shared vehicles. Transport Policy 76, 13e20. Society of Automotive Engineers International, 2014. Automated Driving Levels of Driving Automation Are Defined in New SAE International Standard J3016. Retrieved from: http:// cyberlaw.stanford.edu/files/blogimages/LevelsofDrivingAutomation.pdf. Shabanpour, R., Golshani, N., Shamshiripour, A., Mohammadian, A., 2018. Eliciting preferences for adoption of fully automated vehicles using best-worst analysis. Transportation Research Part C 93, 463e478. Train, K., 2009. Discrete Choice Methods with Simulation, second ed. Cambridge University Press, New York, NY. Tversky, A., Kahneman, D., 1992. Advances in prospect theory: cumulative representation of uncertainty. Journal of Risk and Uncertainty 9, 195e230. Zhang, W., Guhathakurta, S., Khalil, E.B., 2018. The impact of private autonomous vehicles on vehicle ownership and unoccupied VMT generation. Transportation Research Part C 90, 156e165.
Stated ownership and intended in-vehicle time use of privately-owned autonomous vehicles Ying Jianga, Junyi Zhangb, Yinhai Wangc a
Departments of Health Sciences, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan; bGraduate School for International Development and Cooperation, Hiroshima University, Higashi-Hiroshima, Japan; cDepartment of Civil Engineering, University of Washington, Seattle, WA, USA
Chapter outline 1. Introduction 2. Revealed and stated preference survey 2.1 Efforts to enhance the reliability of SP responses 2.2 SP attributes 2.3 SP responses 2.4 Other survey contents 3. Ownership analysis of privatelyowned autonomous vehicles 3.1 Model results and discussion 3.1.1 Random effects of WTP and SP attributes 3.1.2 Other factors 4. Analysis of intended time use inside privately-owned autonomous vehicles
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4.1 Generalized structural equation model (GSEM) 4.2 Modeling results and discussion 5. Conclusion 5.1 Findings about ownership of privately-owned autonomous vehicles 5.2 Findings about time use inside privately-owned autonomous vehicles 5.3 Future research issues Acknowledgments References
585 587 590
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1. Introduction The development of autonomous vehicles (AVs) will provide people with more mobility options. Research on ownership and usage of AVs has therefore Mapping the Travel Behavior Genome. https://doi.org/10.1016/B978-0-12-817340-4.00029-2 Copyright © 2020 Elsevier Inc. All rights reserved.
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become a hot topic in many countries. Accumulation of more scientific evidence is important for the future deployment of AVs. As stated by Zhang et al. (2018), most existing studies analyze the impacts of shared AVs (SAVs) on vehicle ownership; however, limited efforts have been made to understand the impacts of privately-owned AVs (PAVs). Using the 2011 Atlanta Travel Survey data to conduct a simulation analysis, Zhang et al. (2018) found that AVs could allow nearly 20% of households to reduce their owned vehicles while maintaining the current travel patterns, by assuming a 100% AV penetration rate and households’ homogeneous preferences for AVs. Unfortunately, households’ decision-making mechanisms on PAVs ownership have been neglected until now. There are other similar simulation-based studies; however, these studies did not collect data on users’ preferences for autonomous vehicles. Acheampong and Cugurullo (2019) applied socio-psychological and socioecological theories to capture factors affecting the adoption of AVs, and Pettigrew et al. (2019) compared purchase/use intentions of owning a PAV or using an SAV; however, the influence of AV attributes was not reflected in these two studies. Gkartzonikas and Gkritza (2019) made a timely review of existing studies on AVs, in terms of attitude, motivation, perception, belief and confidence, expectation, willingness-to-pay, intention, use of AV as egress transport mode, use of automated features in vehicle, acceptance, adoption, and ownership, etc. Shabanpour et al. (2018) reported that more productive use of in-vehicle time is one of the most common expected benefits of using an AV; however, they did not investigate how use of an AV affects in-vehicle time use. Instead, Shabanpour et al. (2018) implemented a best-worst choice experiment, which used the following attributes to define AV alternatives and asked respondents to make a choice: purchase price, fuel cost to drive, driving range on one tank, overall safety level, emission rate, driver liability, and exclusive lane. To date, no study on time use inside AVs can be found in the literature. PAVs, the target of this study, will dramatically reduce and eventually eliminate human interventions in driving tasks, which will allow human beings to use the time inside PAVs in a more efficient way. In the case of conventional vehicles, drivers usually experience more negative feelings inside vehicles than positive feelings, because they have to operate the vehicle during the whole course of driving. In the case of PAVs, drivers can use more time on non-driving tasks, which are expected to bring more positive feelings to them. However, little is known about such time use from existing studies. To fill the above research gap, a revealed preference (RP) and stated preference (SP) survey was implemented on the Internet to 1002 residents in Japan in 2016. In the RP part, respondents reported their actual time use inside their currently-owned vehicles for both short- and long-distance trips. In the SP part,
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respondents reported their intended in-vehicle time use by referring to the RP time use. Such a two-step survey allows respondents to provide reliable answers. Furthermore, the SP part contains two groups of questions: the first group is about stated choices of four types of vehicles (i.e., conventional vehicle (hereafter, CV), PAV with conditional automation (Conditional PAV), PAV with high automation (High PAV), and PAV with full automation (Full PAV)) under different future scenarios of PAV attributes, and the second group is about the intended time use inside PAVs. Only respondents choosing a PAV were asked to report their intended invehicle time use. In this study, only data from car users (740) were used. For the conventional vehicle, or CV, car users were asked to refer to their currently owned vehicle. In the remaining part of this chapter, the above survey is first introduced. After that, ownership of PAVs is analyzed based on a panel mixed logit model. Next, the intended inside-PAV time use is represented using a multilevel simultaneous-equation negative binomial regression model, and its influential factors are estimated. Finally, this study is concluded, together with a discussion of future research issues.
2. Revealed and stated preference survey For this study, the above RP-SP survey was conducted in September 2016 for both car users and non-car users living in different parts of Japan. As a result, 1002 respondents provided valid data, whose distributions of age and gender were controlled to be the same as those in the populations by region. Here, only data from car users (740) were used.
2.1 Efforts to enhance the reliability of SP responses Before answering the SP questions, each respondent was first asked to report his/her familiarity with autonomous vehicles. It was found that more than 90% of the respondents knew autonomous vehicles to some extent. Second, the driving automation functions of the three types of AVs (Conditional AV, High AV, Full AV) were explained to respondents, as compared with conventional vehicles, based on the text messages from SAE International (2014). Third, respondents were shown a news report dated August 25, 2016, about the release of the first Japanese AV, a minivan “Serena” with ProPilot (less advanced than Conditional AVs), which allows the vehicle to run automatically under a single-lane traffic environment on expressways (in fact, even before 2016, major mass media had already broadcast various news reports about AVs). Fourth, the main advantages and disadvantages of autonomous vehicles were briefly presented to respondents. Fifth, respondents were asked to compare the three types of PAVs (Conditional PAV, High PAV, Full PAV) with their currently owned vehicles (i.e., the choice set contains four
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alternatives) for answering their ownership and usage. They were further asked to answer SP questions by assuming that the current trend of personal income change would continue in the future. As for SP attributes, penetration rates and additional costs of the three types of PAVs, insurance discount rates for PAVs (two attributes: one for Conditional/High PAV and the other for Full PAV), parking cost and timing for the release of PAVs onto the market. The meanings of each SP attribute were explained to respondents before they answered SP questions. With the above survey efforts, respondents were expected to have a better understanding about autonomous vehicles.
2.2 SP attributes Penetration rates were introduced to reflect the potential influence of social interactions on respondents’ choices of the three types of PAVs. Additional costs were determined by referring to existing studies in both Japan and other countries. The additional costs and discount rates of insurance for Full PAV were set to be higher than those of the two other PAVs. Because PAVs can park automatically with the assistance of self-driving and self-parking functions, two levels of parking cost reduction were set. Additional costs, insurance discount rates and permanent parking costs were given based on respondents’ currently-owned vehicles. Finally, the release timing of PAVs to the future market was also assumed. (For details of the SP attributes, refer to Jiang et al., 2018).
2.3 SP responses With the above SP attributes, an orthogonal fractional factorial design was employed and 18 SP profiles were finally obtained. In the survey, the 18 profiles were randomly grouped into six blocks and each respondent was randomly assigned with one block. In other words, each respondent was requested to choose one out of Conditional PAV, High PAV, Full PAV, and the currently-owned vehicle (CV) three times, for short- and/or long-distance driving trips (i.e., vehicle ownership). After answering the three SP profiles, each respondent further reported his/her intended time use inside Conditional/High PAV and/or Full PAV (i.e., vehicle usage), respectively, if a PAV was selected for short- and/or long-distance trips. Items of time use inside PAVs (i.e., multitasking during travel) included monitoring of the movement of car and its surrounding traffic, e-mailing, document-making, reading books/news and comic books, watching TV, enjoying games and Internet surfing, talking (via mobile phone, SNS, or with passengers), sleeping, scenery watching and music listening, smoking, eating, putting on make-up, shaving and so on.
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2.4 Other survey contents In addition to the above SP contents, respondents were also asked to report their willingness to pay (WTP) for Conditional PAV, High PAV, and Full PAV, after giving information about their recent income changes. Furthermore, respondents reported their actual travel behavior and driving experience (unsafe driving incidents during driving, affective feeling, etc.) for short- and/or longdistance driving trips, self-cognition of behavioral changes toward safe driving (i.e., whether and how much he/she wants to improve his/her current driving safety level). Finally, individual attributes were investigated.
3. Ownership analysis of privately-owned autonomous vehicles Among the 740 car users, 576 provided 1728 valid SP responses. As for additional costs when purchasing an AV, respondents were willing to pay 402,233 JPY (i.e., WTP) for a Conditional PAV, on average (22.3% higher than the original price of conventional car), 563,847 JPY (31.9% increase) for a High PAV, and 793,611 JPY (45.7% increase) for a Full PAV, respectively. Excluding zero payments, it is revealed that the WTP values increase to an average of 499,324e934,518 JPY. In the case of the aforementioned Serena with partial driving automation, additional purchasing cost in the Japanese market is currently 243,000 JPY.1 Compared to this value, it can be said that the above WTP values from the survey are reliable. Because each respondent provided multiple SP choices, these choice answers may be correlated. To address such correlations, a panel mixed logit model (McFadden and Train, 2000; Train, 2009; Hole, 2007, 2013) is employed (hereafter, PMXL model). Here, three SP responses reported by each respondent were treated as a panel data.
3.1 Model results and discussion To estimate the PMXL model, the conventional vehicle was selected as a reference. For explanatory variables, WTP for additional purchase cost reported by respondents and the five SP attributes for different types of PAVs are introduced with random-effects, with parameters assumed to be invariant across the three types of PAVs. Driving experience, behavioral change toward safe driving, future income expectation, and individual attributes are further introduced to explain the choices of the three types of PAVs, where their parameters are assumed to be invariant across the three PAVs. STATA software (Version 15.0) was applied to estimate the PMXL model, with 500 Halton draws and 50 burnings. Table 29.1 shows the estimation results, which confirm
1. http://www.nissan.co.jp/SP/SERENA/VLP/OPTION/option_all.html.
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TABLE 29.1 Estimation results of the PAVs ownership model. Parameter
tScore
Sig.a
Mean
0.05
4.98
***
Stdevb
0.16
5.45
***
Interval valuec
[0.26, 0.36]
Mean
0.13
5.82
***
Stdev
0.13
5.93
***
Interval valuec
[0.38, 0.12]
Mean
0.08
2.42
**
Stdev
0.23
4.85
***
Interval valuec
[0.53, 0.37]
Mean
0.02
1.69
*
Stdev
0.03
1.42
Interval valuec
[0.08, 0.04]
Mean
0.08
2.36
**
Stdev
0.27
4.83
***
Interval valuec
[0.61, 0.45]
Mean
0.06
0.47
***
Stdevb
3.60
4.95
Interval valuec
[7.00, 7.12]
Explanatory variables Attributes with random effects WTP for additional purchase cost (10,000 JPY)
SP attributes
Additional purchase cost of PAV (10,000 JPY)
Insurance reduction rated of PAV
Permanent parking cost reduction rated of PAV
Penetration rated of PAV
Release timing of PAV to the market (years)
b
b
b
b
Individual attributes 15e29 years old [aged under 30s] (Yes: 1; No: 0)
7.19
3.29
***
40e49 years old [aged 40s] (Yes: 1; No: 0)
3.18
1.80
*
50e59 years old [aged 50s] (Yes: 1; No: 0)
1.93
0.98
*
60e69 years old [aged 60s] (Yes: 1; No: 0)
4.30
1.87
Gender (Male: 1; Female: 0)
2.08
1.49
High-education (University level or above: 1; otherwise: 0)
3.82
2.72
***
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TABLE 29.1 Estimation results of the PAVs ownership model.dcont’d Parameter
tScore
Sig.a
Number of elderly household members (aged 65þ)
2.76
2.98
***
Number of primary & secondary school students in household
2.88
2.53
**
2.31
1.99
**
exp (absolute value of income decrease)
9.75
3.33
***
ln (absolute value of income increase þ 1)
0.90
0.12
Short-distance driving experience
Sudden braking/handling (Yes: 1; No: 0)
5.71
2.77
***
Driving time per trip (min)
0.11
3.78
***
Driving frequency (times/week)
0.61
1.42
Driving purpose 1: commuting purpose (Yes: 1; No: 0)
0.98
0.42
Driving purpose 2: shopping purpose (Yes: 1; No: 0)
0.59
0.40
Sudden braking/handling (Yes: 1; No: 0)
14.65
4.43
Driving time per trip (min)
0.002
0.48
Driving frequency (times/week)
0.92
0.59
Driving purpose 1: tourism (Yes: 1; No: 0)
2.50
1.76
Driving purpose 2: going back to hometown (Yes: 1; No: 0)
2.84
1.37
Explanatory variables
Behavioral change toward safe driving Stage of driving safety improvement (Try to improve: 1; otherwise: 0) Future expectation of income
Long-distance driving experience
Initial log-likelihood
2395.52
Converged log-likelihood
1309.14
McFadden Rho-squared
0.454
Adjusted McFadden Rho-squared
0.440
a
Significant level: *10%; **5%; ***1%). Standard deviation. Interval estimate of parameter under 95% confident level. d The rate value is the original ratio (%) multiplied by 100 (e.g., 50% is transformed to 50). b c
***
*
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that the model accuracy is sufficiently high, because the McFadden Rho-squared value is 0.454 (the adjusted value is 0.440).
3.1.1 Random effects of WTP and SP attributes Here, a normal distribution is assumed to capture the random effects of WTP and SP attributes. This is because different respondents may show different responses to WTP and SP attributes and positive and negative responses may co-exist among individuals. Such random effects are also a reflection of unobserved heterogeneities. In Table 29.1, interval estimates under 95% confidence level for random-effect variables are also shown. It is observed that four negative mean parameters and two positive mean parameters are obtained; however, interval values of these six parameters cover both negative and positive values. All these random-effect parameters (mean and/or standard deviation parameters) are statistically significant. Looking at the mean parameters, only WTP and the SP attribute “additional purchase cost of PAV” show intuitive results, i.e., the higher the WTP and the cheaper the additional purchase cost of PAV, the higher the probability of choosing an PAV. However, the other unintuitive results do not necessarily imply that relevant estimations are wrong. The standard deviation parameter of “permanent parking cost reduction rate of PAV” is not significant, suggesting that variations of responses to this variable across individuals are not remarkably large, i.e., individuals have similar concerns about permanent parking cost. On the other hand, the mean parameter of “release timing of PAV to the market” is insignificant, but its standard deviation is significant, meaning that even though release timing does not matter to decisions on PAVs ownership on average, this is not true to every individual. Notably, the standard deviation parameter of “release timing of PAV to the market” is the largest among the six randomeffect variables, which is 13e28 times higher than other significant standard deviations. The above results indicate that unobserved heterogeneities play a complicated role in explaining PAV ownership. In other words, heterogeneous preferences of different population groups should be taken into consideration when deploying autonomous vehicles in the market. 3.1.2 Other factors The significant and positive parameter of behavioral change toward safe driving indicates that the attitude of trying to improve current driving safety level is associated with respondents’ higher preferences for autonomous vehicles. Such drivers may find it difficult to improve driving safety by themselves and consequently tend to rely on advanced technologies. As for driving experience, sudden braking/handling experiences in short- and long-distance driving trips play opposite roles in decisions on PAVs ownership. Shortdistance experience discourages the ownership, while long-distance experience encourages the ownership. This may be because short-distance driving
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usually involves more complicated traffic situations and is consequently riskier than long-distance driving, while long-distance driving usually takes place on expressways, which may be less risky than ordinary roads but drivers may be more likely to experience drowsy driving. In addition, drivers are sensitive to driving time in short-distance trips but are insensitive in long-distance trips. As for trip purposes, only the tourism purpose affects PAVs ownership: respondents prefer using a PAV for tourism trips. Driving frequency does not seem to affect people’s preferences for ownership. According to the prospect theory (Tversky and Kahneman, 1992), people are more sensitive to loss than to gain. In line with such a consideration, expected income decrease is transformed into an exponential form and expected income increase into a logarithmic form. The modeling results show that decrease in future income matters to the PAV ownership decision, but the increase does not. The above model results may be a reflection of income change rates (22.1% for those reporting an increase and 49.1% for those reporting a decrease). In total, there are eight variables for individual attributes (age, gender, education, and household structure), among which six are significant. To capture the non-linear effects of age, it is categorized into five dummy variables, where the variable “aged 30s [30e39 years old]” is set as a reference. Aged under 30 show a negative response to autonomous vehicles and other age groups respond positively. Among these age variables, the magnitude (7.19) of “aged under 30” is the largest, which is 3.7 times larger than the smallest (aged 50s: 1.93), indicating its large influence. Gender does not affect the ownership decision. Respondents with high education level prefer to own a PAV. Household structure variables show opposite influences: the parameter of the number of elderly members is negative and that of the number of school students is positive.
4. Analysis of intended time use inside privately-owned autonomous vehicles 4.1 Generalized structural equation model (GSEM) In total, this study classified the intended time use inside autonomous vehicles into the following seven categories, as shown in Table 29.2, where time use data of short-distance and long-distance trips for all three types of autonomous vehicles are pooled together. The RP data show that actual driving-focused time inside vehicles accounts for 56.6% of the whole travel time in the case of short-distance trips and 61.0% in long-distance trips. In contrast, the SP data reveal that for short-distance trips, respondents intended to spend 22.5% and 11.7% of the whole travel time focusing on driving when using a Conditional/High PAV and a Full PAV, respectively. In the case of long-distance trips, the respective shares of the
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TABLE 29.2 Summary of intended time use inside autonomous vehicles.
Variable name
Content
Zero observations
Average time with zeros
Average time without zeros
TimeUse_1
Driving-focused time use (to monitor the movement of car and its surrounding traffic)
152 (18.8%)
21 min
26 min
TimeUse_2
Time use on emailing, documentmaking, reading books/news and comic, etc.
348 (43.0%)
11 min
19 min
TimeUse_3
Time use on watching TV, enjoying games and Internet surfing, etc.
492 (60.8%)
9 min
22 min
TimeUse_4
Time use on talking (via mobile phone, SNS, or with passengers)
167 (20.6%)
24 min
30 min
TimeUse_5
Time use on sleeping
552 (68.2%)
12 min
37 min
TimeUse_6
Time use on scenery watching and music listening
251 (31.0%)
17 min
24 min
TimeUse_7
Time use on other purposes (e.g., smoking, eating, making-up, shaving)
431 (53.3%)
11 min
24 min
intended driving-focused time are 27.0% if a Conditional/High PAV is used and 12.9% if a Full PAV is used. To reflect the influence of the many zero observations, a negative binomial regression model is used to represent each time use variable. The above time use variables were reported only with respect to those SP responses of Conditional PAVs, High PAVs, and Full PAVs. As a result, data used for this part of the study contains 378 car user respondents who provided 809 SP responses. Because of the repeated observations from each respondent, the 809 samples are probably not independent of each other. To address such potential correlation, this study further adopts a multilevel modeling approach by adding an individual-specific error component to the above negative binomial regression
Stated ownership and intended in-vehicle time Chapter | 29
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model. Such error component can also be used to incorporate the influence of unobserved individual heterogeneity. With the above considerations and mathematical treatments, this study jointly estimated the above seven time use models by using the GSEM module of Stata (Version 15.0). GSEM is an abbreviation of generalized structural equation model. GSEM is the combination of generalized linear model (GLM) estimation (McCullaugh and Nelder, 1989) and the SEM estimation (Baum, 2016). It can include not only continuous responses, but also binary, ordinal, count, or multinomial responses, and not only linear regression model, but also gamma regression, logit, probit, ordinal logit/probit, Poisson, negative binomial, and so on.
4.2 Modeling results and discussion In total, three categories of explanatory variables (individual attributes, driving preference, and experience of risky driving) were selected in this study (See Table 29.3). To confirm the model performance (see Table 29.4), this study made a Chisquare test between the multilevel model (converged final loglikelihood ¼ 17537.606) and the fix-effects model (converged final loglikelihood ¼ 18133.246) and found that the test statistic is 1191.28 (¼2*(17537.606 (18133.246))), which is larger than the critical value of 20.09 with the degree of freedom being 8. This indicates that the above-built simultaneous-equation time use model is significantly different from the fixedeffects model at 1% level.
TABLE 29.3 Explanatory variables for GSEM. Category
Detailed explanatory variables
Individual attributes
l l l
Age: actual age (years old) Gender: 1. Male, 0. Female Education: 1. university or above, 0. otherwise
Driving preference
Measured in ordinal scale: 1. definitely yes; 2. probably yes; 3. neutral; 4. probably no; 5. definitely no l Like_drive: like to drive l Drive_more: Willing to drive more l Good_driving: good at driving l Danger_driving: driving is dangerous l Avoid_driving: avoid driving as much as possible
Experience of risky driving
l
l
Short_dis_brake: 1. experienced sudden braking/ handling in short-distance driving, 0. Otherwise Long_dis_brake: 1. experienced sudden braking/ handling in long-distance driving, 0. otherwise
588 PART | II New research methods and findings
TABLE 29.4 Modeling estimation results of the multilevel simultaneous-equation negative binomial regression model. TimeUse_1 Explanatory variables
Parameter
TimeUse_2
TimeUse_3
P > jzj
Var ratio
Parameter
P > jzj
Var ratio
Parameter
P > jzj
Var ratio
****
27.5%
Individual attributes Age
0.029
***
27.7%
0.012
**
6.4%
0.007
Gender
0.715
***
17.8%
0.535
***
13.3%
1.060
Education
0.031
0.0%
0.368
*
6.1%
0.136
0.4%
3.4%
0.342
**
48.8%
0.038
0.3%
12%
Driving preference 0.104
Like_drive Drive_more
0.037
Good_driving
0.244
Danger_driving
0.069
Avoid_driving
0.263
0.4%
0.050
0.9%
0.071
1.0%
***
18.5%
0.160
10.7%
0.258
14.7%
1.6%
0.001
0.0%
0.207
***
25.8%
0.093
4.3%
0.379
***
4.4%
0.054
0.5%
0.558
10.1% **
37.6%
7.2%
Driving experience Short_dis_brake
0.397
Long_dis_brake
0.153
**
0.1%
0.056
9.4%
0.676
**
0.1%
4.022
***
Individual-specific error components: Var [ 0.123** Mean
1.000
3.838
Inalpha
0.839
1.012
***
1.738
Note: (1) * significant at 10% level, ** 5% level, *** 1% level. (2) [Time use variables] TimeUse_1: Driving-focused time use (to monitor the movement of car and its surrounding traffic); TimeUse_2: Time use on e-mailing, document-making, reading books/news and comic, etc.; TimeUse_3: Time use on watching TV, enjoying games and Internet surfing, etc.; TimeUse_4: Time use on talking (via mobile phone, SNS, or with passengers); TimeUse_5: Time use on sleeping; TimeUse_6: Time use on scenery watching and music listening; TimeUse_7: Time use on other purposes (e.g., smoking, eating, making-up, shaving). (3) [Individual attributes] Age: actual age (years old); Gender: 1-male, 0-female; Education: 1-university or above, 0-otherwise. (4) [Driving preference] (1: definitely yes, 2: probably yes, 3: neutral, 4: probably no, 5: definitely no) e Like_drive: like to drive; Drive_more: Willing to drive more; Good_driving: good at driving; Danger_driving: think driving is dangerous; Avoid_driving: avoid driving as much as possible. (5) [Experience of risky driving] Short_dis_brake: 1 e experienced sudden braking/handling in short-distance driving, 0 e otherwise; Long_dis_brake: 1 e experienced sudden braking/handling in long-distance driving, 0 e otherwise.
There are 70 parameters for the 10 explanatory variables, of which 29 are statistically significant at either 10%, 5% or 1% level. Concerning individual attributes, in total, 21 relevant parameters were estimated, of which 11 are significant. Older drivers are more likely to focus on driving (TimeUse_1), spending time on e-mailing, document-making, reading books/news and comic, etc. (TimeUse_2), on talking (via mobile phone, SNS, or with passengers) (TimeUse_4), and on scenery watching and music listening (TimeUse_6). Male drivers prefer more time on driving
589
Stated ownership and intended in-vehicle time Chapter | 29
TABLE 29.4 Modeling estimation results of the multilevel simultaneous-equation negative binomial regression model. TimeUse_4
TimeUse_5
Parameter
P > jzj
Var ratio
Parameter
0.023
***
P > jzj
TimeUse_6
TimeUse_7
Var ratio
Parameter
P > jzj
Var ratio
Parameter
2.1%
0.030
***
33.0%
0.010
9.4%
0.518
***
10.1%
0.637
P> jzj
Var ratio
***
12.8%
28.7%
0.009
0.211
2.5%
0.576
0.067
0.2%
0.430
5.0%
0.169
1.0%
0.175
0.9%
0.127
8.2%
0.228
13.1%
0.044
0.6%
0.216
13.1%
*
0.047
1.0%
0.182
7.3%
0.178
*
9.2%
0.335
0.152
11.8%
0.296
22.2%
0.334
***
37.5%
0.188
0.008 0.269
***
0.0%
0.049
0.7%
0.041
0.6%
0.078
44.1%
0.289
25.1%
0.123
6.1%
0.262
3.3%
**
9.9% 1.8% **
23.2%
6.6%
0.003
0.0%
0.560
7.0%
0.014
0.0%
0.196
0.307
3.5%
0.668
8.1%
0.272
1.8%
0.570
**
3.921
***
3.438 0.425
***
5.724 2.132
***
2.683 0.857
***
27.4%
1.0%
1.447
(TimeUse_1), on e-mailing, document-making, reading books/news and comic, etc. (TimeUse_2), on watching TV, enjoying games and Internet surfing, etc. (TimeUse_3), on sleeping (TimeUse_5), on scenery watching and music listening (TimeUse_6), and on other purposes (TimeUse_7). A degree of university or above is associated with longer time on e-mailing, documentmaking, reading books/news and comic, etc. (TimeUse_2). As for driving preference, drivers preferring more time on driving (TimeUse_1) are those who think they are not good at driving and do not try to avoid driving as much as possible. Sleeping time is only affected by gender.
590 PART | II New research methods and findings
Drivers who like to drive are likely to spend less time on e-mailing, documentmaking, reading books/news and comic, etc.; however, such driving liking attitudes do not matter to other time use decisions. Drivers who are good at driving prefer less time spent on scenery watching and music listening. The driving preference “driving is dangerous” is not influential to any time use decisions. Drivers who try to avoid driving as much as possible prefer less time on enjoying games and Internet surfing, talking, and other purposes. Regarding experience of risky driving in driver’s daily life, drivers experiencing more sudden braking/handling in short-distance driving prefer more time focusing on driving; however, such experience does not matter for other time use decisions. Experiencing more sudden braking/handling in longdistance driving encourages drivers to spend more time on e-mailing, document-making, reading books/news and comic, watching TV, enjoying games and Internet surfing, and other purposes; however, such experience does not matter to time decisions on driving, talking, sleeping, and scenery watching and music listening. The ratio of total variances explained by each of the explanatory variables further informs us about the size of influence of each variable. Time use focusing on driving is more influenced by age (27.7%), gender (17.8%), and driving preferences of Avoid_driving (25.8) and Good_driving (18.5%), than other variables. Driving preferences of Like_drive (48.8%) and gender (13.3%) are more influential to time use on e-mailing, document-making, reading books/news and comic, etc. Time use on watching TV, enjoying games and Internet surfing, etc. is more associated with driving preference of Avoid_drive (37.6%) and gender (27.5%). Driving preference of Avoid_driving (44.1%) and age (28.7%) are the only influential factors, which account for 72.8% of the total variance. Driving preference of Good_driving (37.5%), age (33.0%) and gender (10.1%) are more associated with time use on scenery watching and music listening. Lastly, time use on other purposes is more influenced by driving preferences of Drive_more (27.4%) and Avoid_drive (23.2%) and gender (12.8%).
5. Conclusion Understanding people’s preferences for owning and using autonomous vehicles in the future is crucial for policy makers; however, our knowledge remains very limited because of insufficient research accumulation. For providing additional insights into existing literature, the present study aimed to reveal unknowns about ownership and usage of autonomous vehicles in the context of Japan, based on an SP survey. Different sets of observed factors were selected for explaining ownership and usage of autonomous vehicles. Unobserved heterogeneities are captured by using a mixed logit model to represent the ownership behavior and by building a multilevel simultaneous-equation negative binomial regression model to describe usage behavior.
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5.1 Findings about ownership of privately-owned autonomous vehicles All WTP and SP attributes consistently showed mixed effects on the ownership of autonomous vehicles, i.e., both negative and positive influences are identified. Such results are not unrealistic, considering the existence of various types of persons. This result implies that careful segmentation strategies are required for making cost-related policies. This result also suggests that noncost factors may be more useful to effectively deploy autonomous vehicles in the real world. People trying to improve their driving safety are more likely to own an autonomous vehicle. Experience of risky driving behavior in shortdistance trips reduces the probability of choosing an autonomous vehicle, while experience of risky driving behavior in long-distance trips plays an opposite role. In the case of long-distance trips, the tourism purpose is associated with a higher ownership level than other purposes. Based on the results, daily-trip purposes do not influence ownership. Increase in future income does not matter to decisions on ownership; however, decrease in future income reduces the preference for PAV ownership. Young people tend to avoid owning an autonomous vehicle, while people aged 40s and 60s are more likely to own it. Gender does not matter to decisions regarding ownership. Other persons showing a higher level of ownership preference are those with a university degree or above, having more primary and secondary school students in households, and having fewer elderly members in households.
5.2 Findings about time use inside privately-owned autonomous vehicles First, regarding driving-focused time use, major factors linked to more time concentrating on driving are being male and older persons: accounting for 45.5% of the total variance of driving-focused time use; and the attitudes of “being bad at driving” and “do not avoid driving as much as possible”, which explain 44.3% of the total variance. Thus, enhancing the awareness of driving safety is helpful for drivers to concentrate more time on driving. Second, the preferred sleeping time was 12 min, on average (including those without sleeping). About 70% of respondents did not prefer sleeping while using an autonomous vehicle. Excluding those non-sleeping samples, the average sleeping time preferred was 378 min. It is controversial whether sleeping inside autonomous vehicles could be recommended or not under the driving mode of full automation. Analyses in this study confirmed that shorter sleeping time is only associated with females, but this only explains 9.4% of total variance. This suggests that it is necessary to make more efforts in the future to figure out more influential factors. Third, with respect to other non-driving tasks, as expected, experience of risky driving in long-distance trips consistently increases time spent on e-mailing, document-making, reading books/
592 PART | II New research methods and findings
news and comic, on watching TV, enjoying games and Internet surfing, and on other purposes. Driving automation, which may reduce risky driving, could mitigate negative travel utility in long-distance trips, via more in-vehicle multitasking. Different types of driving preference affect time use on the above non-driving tasks in different ways. Similar differences are also observed with respect to individual attributes.
5.3 Future research issues The findings in this study not only provide unique insights into the deployment of PAVs in terms of policymaking, product design, and marketing, and so on, but also advances time use research associated with travel behavior and PAVs. However, this study revealed mixed effects of PAV-related cost variables. This implies that deployment of autonomous vehicles needs more careful market segmentation strategies; it may also be worth exploring the influence of more non-cost factors. In recent years, mobility as a service (MaaS) has attracted more and more attention, which is defined as “the integration of various forms of transport services into a single mobility service accessible on demand” by the MaaS Alliance.2 Within the context of integrated transport services such as MaaS, people may decide whether to own an autonomous vehicle or not depending on how its usage complements other modes. More relevant research should be conducted in the future, for example, with respect to: shared ownership and usage of autonomous vehicles; as a moving home/office/hotel; combined with various life activities; by considering different needs of different population groups, and so on. The use of autonomous vehicles will make in-vehicle time use more efficient. In this regard, it is worth exploring how to design the inside-vehicle space for facilitating more efficient use of travel time. Efficient use of time inside autonomous vehicles increases the utility of owning an autonomous vehicle; as a result, more cars may be observed on roads, if effective countermeasures are not taken. Shared use of autonomous vehicles and seamless transfer between autonomous vehicles and other travel modes may need to be promoted from a perspective of social desirability.
Acknowledgments This study was fully supported by the Grants-in-Aid for Scientific Research (A), Japan Society for the Promotion of Science (JSPS) for the project titled “Interdisciplinary Research on Policies Promoting Young People’s Migration to and Permanent Residence in Local Cities” (Principal researcher: Junyi Zhang, Hiroshima University; 15H02271).
2. https://maas-alliance.eu/homepage/what-is-maas/.
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