Potential Impacts of Autonomous Vehicles on Urban Sprawl: A Comparison of Chinese and US Car-Oriented Adults
Abstract
:1. Introduction
2. Literature Review
3. Methodology
3.1. Survey Design
- (1)
- Questions regarding respondents’ knowledge of and preferences for AVs. We asked the following: “How much would you say that you know about self-driving cars?” and “Suppose you had a self-driving car that would let you move from your current home farther away from the nearest city or farther away from the destination for your most-frequent trip. In the self-driving car, even if you were farther away, the amount of time the trip would take would be the same, and you might be able to do other things when in the self-driving car. How likely would you be to consider moving farther away?” Responses were captured using five-point Likert scale. These two items were dependent variables in this analysis.
- (2)
- Socio-economic and demographic questions to describe respondents included age, gender, country, employment status, family situation, health, education level, residential location and annual household income level.
- (3)
- Questions about features in people’s current cars. These questions included vehicle cost, car purchasing time, new or pre-owned when bought, and the presence of different technologies that could take on the automatic functions of the driving task (e.g., automated lane keeping, pilot assist, parking assist, automatic cruise control).
- (4)
- Questions about the characteristics of the most-frequent trip. These included importance of the vehicle for people, respondents’ travel time in vehicles, access time to vehicle form start point and parking issues.
- (5)
- Attitudinal statements about people’s preferences for AVs. Statements included overall attitudes toward AVs, the transportation environment, driving flexibility, and new technologies. Responses were measured with a five-point Likert scale. For the analysis, items were coded into dummy variables to avoid a heterogeneity problem: respondents’ preference choices of 1, 2 or 3 were coded as 0 (more negative attitude toward), and 4 and 5 were coded as 1 (more positive attitude toward).
3.2. Survey Data
3.3. Ordered Logistic Regression Model
4. Results and Discussions
4.1. Descriptive Analysis
4.2. Ordered Logistic Regression Model Results and Analysis: Knowledge of AVs
4.3. Impact of AVs on People’s Likelihood of Moving Farther
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Gurumurthy, K.M.; Kockelman, K.M. Modeling Americans’ autonomous vehicle preferences: A Focus on Dynamic Ride-Sharing, Privacy & Long-Distance Mode Choices. In Proceedings of the 98th Annual Meeting of the Transportation Research Board, Washington, DC, USA, 13 January 2019. [Google Scholar]
- Stocker, A.; Shaheen, S. Shared automated vehicle (SAV) pilots and automated vehicle policy in the U.S.: Current and future developments. In Road Vehicle Automation 5, Lecture Notes in Mobility; Meyer, G., Beiker, S., Eds.; Springer International Publishing: Berlin/Heidelberg, Germany, 2019; pp. 131–147. [Google Scholar]
- Wadud, Z.; MacKenzie, D.; Leiby, P. Help or hindrance? The travel, energy and carbon impacts of highly auto-mated vehicles. Transp. Res. Part A Policy Prac. 2016, 86, 1–18. [Google Scholar] [CrossRef] [Green Version]
- Heinrichs, D. Autonomous Driving and Urban Land Use. In Autonomous Driving; Maurer, M., Gerdes, J., Lenz, B., Winner, H., Eds.; Springer: Berlin/Heidelberg, Germany, 2016. [Google Scholar]
- Gucwa, M. The Mobility and Energy Impacts of Automated Cars. Master’s Thesis, Stanford University, Standford, CA, USA, 2014. [Google Scholar]
- Childress, S.; Nichols, B.; Coe, S. Using an activity-based model to explore possible impacts of automated vehicles. Transportation Research Record. J. Transp. Res. Board 2015, 2493, 99–106. [Google Scholar] [CrossRef]
- Fagnant, D.J.; Kockelman, K. Preparing a nation for autonomous vehicles: Opportunities, barriers and policy recommendations. Transp. Res. Part A Policy Pract. 2015, 77, 167–181. [Google Scholar] [CrossRef]
- Cervero, R.; Kockelman, K. Travel demand and the 3Ds: Density, diversity, and design. Transp. Res. Part D Transp. Environ. 1997, 2, 199–219. [Google Scholar] [CrossRef]
- Guan, J.; Yang, D. Residents’ Characteristics and Transport Policy Analysis in Large-Scale Residential Areas on a City Pe-riphery: Case Study of Jinhexincheng, Shanghai, China. Transp. Res. Rec. J. Transp. Res. Board 2015, 2512, 11–21. [Google Scholar] [CrossRef]
- Guan, J.; Xu, C. Are relocatees different from others? Relocatee’s travel mode choice and travel equity analysis in large-scale residential areas on the periphery of megacity Shanghai, China. Transp. Res. Part A: Policy Pr. 2018, 111, 162–173. [Google Scholar] [CrossRef]
- Center Intelligence Agency. Country Area Comparison. 2019. Available online: https://www.cia.gov/library/publications/the-world-factbook/rankorder/2147rank.html (accessed on 1 August 2019).
- United States Census Bureau. 2018 US National Population. 2019. Available online: https://www.census.gov/library/visualizations/interactive/population-increase-2018.html (accessed on 1 August 2019).
- National Data of National Bureau of Statistics. Population Age Structure in China 2019. Available online: https://data.stats.gov.cn/easyquery.htm?cn=C01 (accessed on 30 September 2020).
- Statistics Times. 2021. Available online: https://statisticstimes.com/economy/united-states-vs-china-economy.php (accessed on 1 July 2021).
- West, D.M. Moving Forward: Self-Driving Vehicles in China, Europe, Japan, Korea, and The United States; Center for Technology Innovation at Brookings: Washington, DC, USA, 2016. [Google Scholar]
- Trommer, S.; Kolarova, V.; Fraedrich, E.; Kröger, L.; Kickhöfer, B.; Kuhnimhof, T.; Phleps, P. Autonomous Driving—The Impact of Vehicle Automation on Mobility Behavior Techincal Report. 2016. Available online: https://elib.dlr.de/110337/1/ifmo_2016_Autonomous_Driving_2035_en.pdf (accessed on 1 July 2021).
- SAE International. Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles; SAE International: Warrendale, PA, USA, 2016. [Google Scholar]
- Talebpour, A.; Mahmassani, H.S. Influence of connected and autonomous vehicles on traffic flow stability and throughput. Transp. Res. Part C: Emerg. Technol. 2016, 71, 143–163. [Google Scholar] [CrossRef]
- Cui, S.; Seibold, B.; Stern, R.; Work, D. Stabilizing traffic flow via a single autonomous vehicle: Possibilities and limitations. In Proceedings of the 2017 IEEE Intelligent Vehicles Symposium (IV); Institute of Electrical and Electronics Engineers (IEEE), Redondo Beach, CA, USA, 11–14 June 2017; pp. 1336–1341. [Google Scholar]
- Ma, K.; Wang, H. Influence of Exclusive Lanes for Connected and Autonomous Vehicles on Freeway Traffic Flow. IEEE Access 2019, 7, 50168–50178. [Google Scholar] [CrossRef]
- Zheng, Y.; Wang, J.; Li, K. Smoothing Traffic Flow via Control of Autonomous Vehicles. IEEE Internet Things J. 2020, 7, 3882–3896. [Google Scholar] [CrossRef] [Green Version]
- Harper, C.D.; Hendrickson, C.T.; Mangones, S.; Samaras, C. Estimating potential increases in travel with autonomous vehicles for the non-driving, elderly and people with travel-restrictive medical conditions. Transp. Res. Part C Emerg. Technol. 2016, 72, 1–9. [Google Scholar] [CrossRef]
- Moreno, A.T.; Michalski, A.; Llorca, C.; Moeckel, R. Autonomous Taxis Effect on Vehicle-Km Traveled and Average Trip 4 Duration in the Greater Munich Metropolitan Area. In Proceedings of the 97th Annual Meeting of the Transportation Re-search Board, Washington, DC, USA, 7–11 January 2018. [Google Scholar]
- Harb, M.; Xiao, Y.; Circella, G.; Mokhtarian, P.L.; Walker, J.L. Projecting Travelers into a World of Self-Driving Vehicles: Naturalistic Experiment for Travel Behavior Implications. Transportation 2018, 45, 1671–1685. [Google Scholar] [CrossRef]
- Krueger, R.; Rashidi, T.H.; Rose, J.M. Preferences for shared autonomous vehicles. Transp. Res. Part C: Emerg. Technol. 2016, 69, 343–355. [Google Scholar] [CrossRef]
- Haboucha, C.J.; Ishaq, R.; Shiftan, Y. User preferences regarding autonomous vehicles. Transp. Res. Part C: Emerg. Technol. 2017, 78, 37–49. [Google Scholar] [CrossRef]
- Winter, K.; Cats, O.; Martens, K.; van Arem, B. A Stated-Choice Experiment on Mode Choice in an Era of Free-Floating Carsharing and Shared Autonomous Vehicles. In Proceedings of the 96th Annual Meeting of the Transportation Research Board, Washington, DC, USA, 8–12 January 2017; pp. 1–17. [Google Scholar]
- Steck, F.; Kolarova, V.; Bahamonde-Birke, F.; Trommer, S.; Lenz, B. How Autonomous Driving May Affect the Value of Travel Time Savings for Commuting. Transp. Res. Rec. J. Transp. Res. Board 2018, 2672, 11–20. [Google Scholar] [CrossRef] [Green Version]
- Nazari, F.; Noruzoliaee, M.; Mohammadian, A. Shared Mobility Versus Private Car Ownership: A Multivariate Analysis of Public Interest in Autonomous Vehicles. In Proceedings of the 97th Annual Meeting of the Transportation Research Board, Washington, DC, USA, 7–11 January 2018. [Google Scholar]
- Chen, T.D.; Kockelman, K.M. Management of a Shared Autonomous Electric Vehicle Fleet: Implications of Pricing Schemes. Transp. Res. Rec. J. Transp. Res. Board 2016, 2572, 37–46. [Google Scholar] [CrossRef]
- Levin, M.W.; Boyles, S.D. Effects of Autonomous Vehicle Ownership on Trip, Mode, and Route Choice. Transp. Res. Rec. J. Transp. Res. Board 2015, 2493, 29–38. [Google Scholar] [CrossRef]
- Milakis, D.; van Arem, B.; Van Wee, B. Policy and society related implications of automated driving: A review of literature and directions for future research. J. Intell. Transp. Syst. 2017, 21, 324–348. [Google Scholar] [CrossRef]
- Barbour, N.; Menon, N.; Zhang, Y.; Mannering, F. Shared automated vehicles: A statistical analysis of consumer use likelihoods and concerns. Transp. Policy 2019, 80, 86–93. [Google Scholar] [CrossRef]
- Jing, P.; Huang, H.; Ran, B.; Zhan, F.; Shi, Y. Exploring the Factors Affecting Mode Choice Intention of Autonomous Vehicle Based on an Extended Theory of Planned Behavior—A Case Study in China. Sustainability 2019, 11, 1155. [Google Scholar] [CrossRef] [Green Version]
- Saeed, T.U.; Burris, M.; Labi, S.; Sinha, K.C. An empirical discourse on forecasting the use of autonomous vehicles using consumers’ preferences. Technol. Forecast. Soc. Chang. 2020, 158, 120130. [Google Scholar] [CrossRef]
- Bansal, P.; Kockelman, K.M. Forecasting Americans’ long-term adoption of Gconnected and autonomous vehicle technologies. Transp. Res. Part A Policy Prac. 2017, 95, 49–63. [Google Scholar] [CrossRef]
- Sommer, K. Continental Mobility Study. 2013. Available online: https://www.continental.com/en/press/initiatives-surveys/continental-mobility-studies/mobility-study-2013 (accessed on 1 August 2019).
- Schoettle, B.; Sivak, M. Public Opinion about Self-Driving Vehicles in China, India, Japan, the U.S., the U.K., and Australia. The University of Michigan Transportation Research Institute: Ann Arbor, MI, USA. 2014. Available online: http://deepblue.lib.umich.edu/bitstream/handle/2027.42/109433/103139.pdf?sequence=1&isAllowed=y (accessed on 1 July 2021).
- Schoettle, B.; Sivak, M. A Survey of Public Opinion about Autonomous and Self-Driving Vehicles in the U.S., the U.K., and Australia; UMTRI-2014-21; The University of Michigan Transportation Research Institute: Ann Arbor, MI, USA, 2014. [Google Scholar]
- Bansal, P.; Kockelman, K.M.; Singh, A. Assessing public opinions of and interest in new vehicle technologies: An Austin perspective. Transp. Res. Part C: Emerg. Technol. 2016, 67, 1–14. [Google Scholar] [CrossRef]
- Liu, P.; Yang, R.; Xu, Z. Public Acceptance of Fully Automated Driving: Effects of Social Trust and Risk/Benefit Perceptions. Risk Anal. 2018, 39, 326–341. [Google Scholar] [CrossRef] [PubMed]
- Raue, M.; D’Ambrosio, L.A.; Ward, C.; Lee, C.; Jacquillat, C.; Coughlin, J.F. The Influence of Feelings While Driving Regular Cars on the Perception and Acceptance of Self-Driving Cars. Risk Anal. 2019, 39, 358–374. [Google Scholar] [CrossRef]
- Moore, M.; Lavieri, P.S.; Dias, F.F.; Bhat, C.R. On investigating the potential effects of private autonomous vehicle use on home/work relocations and commute times. Transp. Res. Part C: Emerg. Technol. 2020, 110, 166–185. [Google Scholar] [CrossRef]
- Zhang, W.; Guhathakurta, S. Residential Location Choice in the Era of Shared Autonomous Vehicles. J. Plan. Educ. Res. 2021, 41, 135–148. [Google Scholar] [CrossRef] [Green Version]
- Qualtrics, Provo, UT. Available online: http://www.qualtrics.com (accessed on 1 July 2021).
- U.S. Department of Transportation, Federal Highway Administration, National Household Travel Survey. 2017. Available online: http://nhts.ornl.gov (accessed on 1 August 2019).
- Ministry of Public Security of the People’s Republic of China. China’s Car Ownership Exceeded 200 Million for the First Time in 2018. 2019. Available online: http://www.mps.gov.cn/n2254098/n4904352/c6354939/content.html (accessed on 1 August 2019).
- Statista. Licensed Drivers in the United States from 2013 to 2017, by Age. 2018. Available online: https://www.statista.com/statistics/206311/total-number-of-us-licensed-drivers-in-2010-by-age/ (accessed on 1 August 2019).
- Statista. Total Number of Licensed Drivers in the United States in 2017, by Gender. 2018. Available online: https://www.statista.com/statistics/198017/total-number-of-us-licensed-drivers-in-2009-by-gender/ (accessed on 1 August 2019).
- Norman, P. Putting Iterative Proportional Fitting on the researcher’s desk. Sch. Geogr. Work. Pap. 1999, 99, 1–32. [Google Scholar]
- Guan, J.; Zhang, K.; Shen, Q.; He, Y. Dynamic Modal Accessibility Gap: Measurement and Application Using Travel Routes Data. Transp. Res. Part D: Transp. Environ. 2020, 81, 102272. [Google Scholar] [CrossRef]
- Guan, J.; Zhang, K.; Zhang, S.; Chen, Y. How is public transit in the megacity peripheral relocatees’ area in China? Captive transit rider and dynamic modal accessibility gap analytics in a peripheral large-scale residential area in Shanghai, China. J. Transp. Land Use 2020, 13, 1–21. [Google Scholar] [CrossRef]
- Mao, H.; Fan, X.; Guan, J.; Chen, Y.-C.; Su, H.; Shi, W.; Zhao, Y.; Wang, Y.; Xu, C. Customer attractiveness evaluation and classification of urban commercial centers by crowd intelligence. Comput. Hum. Behav. 2019, 100, 218–230. [Google Scholar] [CrossRef]
Characteristics | China (N = 555) | US (N = 1241) | ||||
---|---|---|---|---|---|---|
N | Percentage | Weighted Percentage | N | Percentage | Weighted Percentage | |
Gender | ||||||
Male | 321 | 57.84% | 69.87% | 606 | 48.83% | 49.38% |
Female | 234 | 42.16% | 30.13% | 635 | 51.17% | 50.62% |
Year of birth | ||||||
≤1945 | 0 | 0.00% | 0.00% | 171 | 13.78% | 7.48% |
1946–1955 | 9 | 1.62% | 1.80% | 202 | 16.28% | 12.57% |
1956–1964 | 118 | 21.26% | 9.26% | 206 | 16.60% | 18.01% |
1965–1980 | 143 | 25.77% | 27.71% | 201 | 16.20% | 26.24% |
1981–1990 | 144 | 25.95% | 34.55% | 228 | 18.37% | 17.91% |
1991–1999 | 141 | 25.41% | 26.67% | 233 | 18.78% | 17.79% |
Marital status | ||||||
Married | 389 | 70.09% | 68.60% | 671 | 54.07% | 53.48% |
Other | 166 | 29.91% | 31.40% | 570 | 45.93% | 46.52% |
Presence of child in the household | ||||||
Have one or more | 241 | 43.42% | 46.62% | 264 | 21.27% | 24.92% |
Do not have any | 314 | 56.58% | 53.38% | 977 | 78.73% | 75.08% |
Car ownership | ||||||
Own | 510 | 91.89% | 91.73% | 1136 | 91.54% | 91.60% |
Lease or other | 45 | 8.11% | 8.27% | 105 | 8.46% | 8.40% |
New or pre-owned when current car was bought | ||||||
New | 509 | 91.71% | 90.92% | 701 | 56.49% | 55.02% |
Pre-owned | 46 | 8.29% | 9.08% | 540 | 43.51% | 44.98% |
Home ownership | ||||||
Own | 493 | 88.83% | 87.53% | 909 | 73.25% | 72.53% |
Rent or other | 62 | 11.17% | 12.47% | 332 | 26.75% | 27.47% |
Physical challenge | ||||||
Challenged | 5 | 0.90% | 1.01% | 65 | 5.24% | 5.52% |
Not challenged | 550 | 99.10% | 98.99% | 1176 | 94.76% | 94.48% |
Residential location | ||||||
Downtown in a large city | 109 | 19.64% | 21.18% | 133 | 10.72% | 11.37% |
Suburban area | 65 | 11.71% | 12.60% | 491 | 39.56% | 37.97% |
Mid-sized city | 239 | 43.06% | 39.18% | 177 | 14.26% | 14.26% |
Small city | 129 | 23.24% | 24.33% | 194 | 15.63% | 15.69% |
Rural area | 13 | 2.34% | 2.71% | 246 | 19.82% | 20.71% |
Employment status | ||||||
Employed | 532 | 95.86% | 97.36% | 690 | 55.60% | 61.00% |
Not employed | 23 | 4.14% | 2.64% | 551 | 44.40% | 39.00% |
Highest level of education completed | ||||||
≤High school graduate | 57 | 10.27% | 8.83% | 220 | 17.73% | 18.26% |
Bachelor’s degree | 329 | 59.28% | 56.97% | 788 | 63.50% | 63.94% |
≥Graduate degree | 169 | 30.45% | 34.21% | 233 | 18.78% | 17.80% |
Level of annual household income before taxes | ||||||
Income level 1 | 186 | 33.51% | 34.92% | 395 | 31.83% | 32.03% |
Income level 2 | 236 | 42.52% | 41.35% | 413 | 33.28% | 33.21% |
Income level 3 | 106 | 19.10% | 18.40% | 272 | 21.92% | 21.71% |
Income level 4 | 27 | 4.86% | 5.34% | 161 | 12.97% | 13.04% |
3 Models: | Combined Sample | CN a Sample Only | US Sample Only | ||||
---|---|---|---|---|---|---|---|
Variable | Coef. | St.Err. | Coef. | St.Err. | Coef. | St.Err. | |
Socio-economic characteristics | |||||||
Age | CN | −0.131 | 0.087 | −0.175 | 0.093 *,b | ||
US | −0.173 | 0.039 *** | −0.158 | 0.039 *** | |||
Male dummy c | CN | 0.370 | 0.164 ** | 0.415 | 0.176 ** | ||
US | 1.005 | 0.114 *** | 0.963 | 0.113 *** | |||
Car ownership dummy | CN | 0.655 | 0.301 ** | 0.792 | 0.336 ** | ||
US | 0.500 | 0.195 ** | 0.490 | 0.195 ** | |||
Married dummy | CN | −0.030 | 0.237 | 0.000 | 0.245 | ||
US | −0.243 | 0.126 * | −0.226 | 0.125* | |||
Presence of child in the | CN | −0.237 | 0.178 | −0.268 | 0.188 | ||
household dummy | US | 0.383 | 0.154 ** | 0.395 | 0.153 ** | ||
Own home dummy | CN | −0.502 | 0.281 * | −0.459 | 0.291 | ||
US | 0.087 | 0.141 | 0.087 | 0.139 | |||
Physical challenged dummy | CN | 2.361 | 0.924 ** | 2.191 | 0.917 ** | ||
US | 0.305 | 0.250 | 0.282 | 0.246 | |||
Income level 1 | 0.000 | fixed | 0.000 | fixed | 0.000 | fixed | |
Income level 2 | CN | −0.246 | 0.198 | −0.241 | 0.209 | ||
US | 0.005 | 0.139 | 0.011 | 0.137 | |||
Income level 3 | CN | 0.020 | 0.248 | 0.051 | 0.259 | ||
US | 0.246 | 0.164 | 0.251 | 0.163 | |||
Income level 4 | CN | 0.370 | 0.411 | 0.484 | 0.417 | ||
US | 0.378 | 0.204 * | 0.391 | 0.202 * | |||
Rural area | 0.000 | fixed | 0.000 | fixed | 0.000 | fixed | |
Downtown in a large city | CN | 1.527 | 0.425 *** | 1.900 | 0.627 *** | ||
US | 0.740 | 0.231 *** | 0.669 | 0.232 *** | |||
Suburban area | CN | 1.630 | 0.454 *** | 2.058 | 0.636 *** | ||
US | 0.075 | 0.152 | −0.015 | 0.152 | |||
Mid-sized city | CN | 1.204 | 0.409 *** | 1.584 | 0.603 *** | ||
US | 0.047 | 0.192 | −0.045 | 0.192 | |||
Small city | CN | 1.228 | 0.416 *** | 1.594 | 0.607 *** | ||
US | 0.130 | 0.184 | 0.063 | 0.183 | |||
Vehicle features | |||||||
Automated lane keeping | CN | 0.387 | 0.225 * | 0.400 | 0.234 * | ||
dummy | US | 0.077 | 0.200 | 0.099 | 0.200 | ||
Pilot assist dummy | CN | 0.011 | 0.206 | 0.031 | 0.216 | ||
US | 0.598 | 0.209 *** | 0.558 | 0.211 *** | |||
New car when bought | CN | −0.174 | 0.289 | −0.189 | 0.308 | ||
dummy | US | 0.454 | 0.120 *** | 0.417 | 0.119 *** | ||
Traveler’s trip characteristics | |||||||
Access time to vehicle (min) | CN | −0.001 | 0.008 | 0.000 | 0.009 | ||
US | 0.010 | 0.004 ** | 0.010 | 0.004 ** | |||
Driving miles weekly (mi) | CN | 0.002 | 0.001 ** | 0.002 | 0.001 ** | ||
US | −0.001 | 0.001 | 0.000 | 0.000 | |||
Travel time in vehicle(min) | CN | 0.007 | 0.004 * | 0.007 | 0.004 * | ||
US | 0.001 | 0.002 | 0.000 | 0.002 | |||
Car importance | CN | 0.079 | 0.110 | 0.196 | 0.119 | ||
US | −0.239 | 0.067 *** | −0.276 | 0.073 *** | |||
Attitudes or perceptions | |||||||
Willing to purchase AVs | CN | 0.239 | 0.199 | 0.347 | 0.211 | ||
dummy | US | 0.283 | 0.126 ** | 0.258 | 0.125 ** | ||
Confidence in learning new technologies in a new vehicle dummy | CN | 0.520 | 0.202 ** | 0.609 | 0.220 *** | ||
US | 0.553 | 0.133 *** | 0.488 | 0.131 *** | |||
Experience with automated driving tech dummy | CN | 0.509 | 0.190 *** | 0.511 | 0.199 ** | ||
US | 1.008 | 0.141 *** | 1.028 | 0.142 *** | |||
Satisfaction with the tech features in the current vehicle dummy | CN | −0.341 | 0.168 ** | −0.361 | 0.177 ** | ||
US | −0.011 | 0.129 | −0.012 | 0.128 | |||
Number of observations | 1796 | 555 | 1241 | ||||
Pseudo r-squared | 0.118 | 0.078 | 0.134 |
3 Models | Combined Sample | CN a Sample Only | US Sample Only | ||||
---|---|---|---|---|---|---|---|
Variable | Coef. | St.Err. | Coef. | St.Err. | Coef. | St.Err. | |
Socio-economic characteristics | |||||||
Age | CN | 0.069 | 0.086 | 0.087 | 0.093 | ||
US | −0.341 | 0.042 *** | −0.323 | 0.042 ***,b | |||
Male dummy c | CN | −0.147 | 0.157 | −0.154 | 0.165 | ||
US | 0.340 | 0.113 *** | 0.318 | 0.112 *** | |||
Car ownership dummy | CN | 0.321 | 0.284 | 0.357 | 0.302 | ||
US | 0.365 | 0.193 * | 0.346 | 0.193* | |||
Married dummy | CN | −0.138 | 0.209 | −0.185 | 0.218 | ||
US | 0.224 | 0.117 * | 0.210 | 0.115 * | |||
Employed dummy | CN | −0.063 | 0.376 | −0.001 | 0.438 | ||
US | 0.259 | 0.128 ** | 0.245 | 0.128 * | |||
Own house dummy | CN | −0.205 | 0.263 | −0.239 | 0.276 | ||
US | −0.340 | 0.133 ** | −0.323 | 0.131 ** | |||
Rural area | 0.000 | fixed | 0.000 | fixed | 0.000 | fixed | |
Downtown in a large city | CN | 0.442 | 0.418 | 0.500 | 0.536 | ||
US | 0.896 | 0.220 *** | 0.845 | 0.218 *** | |||
Suburban area | CN | 0.484 | 0.444 | 0.556 | 0.558 | ||
US | 0.075 | 0.151 | 0.072 | 0.150 | |||
Mid-sized city | CN | 0.805 | 0.412 * | 0.905 | 0.516 * | ||
US | 0.025 | 0.193 | 0.027 | 0.191 | |||
Small city | CN | 0.824 | 0.417 ** | 0.923 | 0.528 * | ||
US | 0.076 | 0.184 | 0.075 | 0.182 | |||
Traveler’s trip characteristics | |||||||
Travel time in vehicle (min) | CN | 0.009 | 0.003 *** | 0.010 | 0.003 *** | ||
US | 0.004 | 0.002 ** | 0.004 | 0.002 ** | |||
Access time to vehicle (min) | CN | 0.002 | 0.008 | 0.003 | 0.008 | ||
US | 0.018 | 0.004 *** | 0.017 | 0.004 *** | |||
Driving miles weekly (mi) | CN | −0.002 | 0.001 ** | −0.002 | 0.001 *** | ||
US | 0.001 | 0.001 | 0.001 | 0.001 | |||
Attitudes or perceptions | |||||||
Acceptance of maximum | CN | 0.277 | 0.156 * | 0.321 | 0.164 ** | ||
automation’s level (5 levels) | US | 0.875 | 0.140 *** | 0.826 | 0.139 *** | ||
Willing to purchase AVs | CN | 0.287 | 0.089 *** | 0.337 | 0.103 *** | ||
dummy | US | 0.381 | 0.057 *** | 0.358 | 0.058 *** | ||
Confidence in learning new technologies in a new | CN | 0.596 | 0.194 *** | 0.667 | 0.205 *** | ||
Vehicle dummy | US | 0.455 | 0.131 *** | 0.432 | 0.130 *** | ||
Experience with automated | CN | 0.376 | 0.184 ** | 0.427 | 0.192 ** | ||
driving tech dummy | US | 0.433 | 0.139 *** | 0.422 | 0.137 *** | ||
Satisfaction with the tech features in the current | CN | −0.363 | 0.159 ** | −0.413 | 0.167 ** | ||
vehicle dummy | US | −0.364 | 0.127 *** | −0.345 | 0.126 *** | ||
Knowledge of AVs | CN | −0.058 | 0.256 | −0.062 | 0.265 | ||
dummy | US | 0.510 | 0.169 *** | 0.482 | 0.167 *** | ||
Number of observations | 1796 | 555 | 1241 | ||||
Pseudo r-squared | 0.128 | 0.044 | 0.139 |
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Guan, J.; Zhang, S.; D’Ambrosio, L.A.; Zhang, K.; Coughlin, J.F. Potential Impacts of Autonomous Vehicles on Urban Sprawl: A Comparison of Chinese and US Car-Oriented Adults. Sustainability 2021, 13, 7632. https://doi.org/10.3390/su13147632
Guan J, Zhang S, D’Ambrosio LA, Zhang K, Coughlin JF. Potential Impacts of Autonomous Vehicles on Urban Sprawl: A Comparison of Chinese and US Car-Oriented Adults. Sustainability. 2021; 13(14):7632. https://doi.org/10.3390/su13147632
Chicago/Turabian StyleGuan, Jinping, Shuang Zhang, Lisa A. D’Ambrosio, Kai Zhang, and Joseph F. Coughlin. 2021. "Potential Impacts of Autonomous Vehicles on Urban Sprawl: A Comparison of Chinese and US Car-Oriented Adults" Sustainability 13, no. 14: 7632. https://doi.org/10.3390/su13147632