Let's Connect
Follow Us
Watch Us
(+385) 99 251 8510
journal.prometfpz.unizg.hr
Traffic&Transportation Journal

Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

PUBLISHED
31.10.2024
LICENSE
Copyright (c) 2024 Wei LI, Tian’ai LI, Yongying MENG, Xuecai XU, Zhifeng MA

College Students’ Cognition and Attitude Towards Connected and Autonomous Vehicles in China: an Exploratory Study

Authors:Wei LI, Tian’ai LI, Yongying MENG, Xuecai XU, Zhifeng MA

Abstract

This study intended to explore college students’ cognition and attitudes towards connected and autonomous vehicles (CAVs) in China. A comprehensive questionnaire was designed and distributed in Mainland China, and after collecting and processing the data, Bayesian multivariate analysis was presented to evaluate the six dimensions of cognition, consciousness, safety, privacy, liability, education and acceptance. By analysing each dimension, the results show that gender and status are significant for consciousness, safety, privacy and education, but location plays a significant role in safety and liability. It is found that each dimension reveals a specific thought of college students, and the potential users’ cognition and attitude should be paid more attention to. Some empirical suggestions are presented to enhance the systematic improvement of CAVs and possible ethics issues.

Keywords:connected and autonomous vehicles, college students, cognition, attitude, Bayesian multivariate analysis

References

  1. [1] Talebpour A, Mahmassani HS. Influence of connected and autonomous vehicles on traffic flow stability and throughput. Transportation Research Part C: Emerging Technologies. 2016;71:143–163. DOI:10.1016/j.trc.2016.07.007.
  2. [2] Bagloee SA, et al. Autonomous vehicles: Challenges, opportunities, and future implications for transportation policies. Journal of Modern Transportation. 2016;24(4):284–303.
  3. [3] Gawron JH, et al. Life cycle assessment of connected and automated vehicles: Sensing and computing subsystem and vehicle. Environmental Science and Technology. 2018;52(5):3249-3256. DOI:10.1021/acs.est.7b04576.
  4. [4] Eagly AH, Chaiken S. The psychology of attitudes. Journal of Marketing Research. 1997;34(2):298–303.
  5. [5] König M, Neumayr L. Users’ resistance towards radical innovations: The case of the self-driving car. Transportation Research Part F: Traffic Psychology and Behaviour. 2017;44:42–52. DOI: 10.1016/j.trf.2016.10.013.
  6. [6] Woldeamanuel M, Nguyen D. Perceived benefits and concerns of autonomous vehicles: An exploratory study of millennials’ sentiments of an emerging market. Research in Transportation Economics. 2018;71:44–53. DOI:10.1016/j.retrec.2018.06.006.
  7. [7] Moody J, et al. Public perceptions of autonomous vehicle safety: An international comparison. Safety Science. 2020;121:634–650. DOI: 10.1016/j.ssci.2019.07.022.
  8. [8] Fu X, et al. How do college students perceive future shared mobility with autonomous vehicles? A survey of the University of Alabama students. International Journal of Transportation Science and Technology. 2022;11(2):189-204.
  9. [9] Othman K. Impact of prior knowledge about autonomous vehicles on the public attitude. Civil Engineering Journal-Tehran. 2023;9(4):990–1006. DOI:10.28991/CEJ-2023-09-04-017.
  10. [10] Morando MM, et al. Studying the safety impact of autonomous vehicles using simulation-based surrogate safety measures. Journal of Advanced Transportation. 2018;11:6135183. DOI:10.1155/2018/6135183.
  11. [11] Cui J, et al. A review on safety failures, security attacks, and available countermeasures for autonomous vehicles. Ad Hoc Networks. 2019;90:101823. DOI:10.1016/j.adhoc.2018.12.006.
  12. [12] Sun X, et al. A Survey on cyber-security of connected and autonomous vehicles (CAVs), IEEE Transactions on Intelligent Transportation Systems. 2022;23(7):6240–6259. DOI:10.1109/TITS.2021.3085297.
  13. [13] Maeng K, et al. Consumers’ attitudes toward information security threats against connected and autonomous vehicles. Telematics and Informatics. 2021;63:101646. DOI: 10.1016/j.tele.2021.101646.
  14. [14] Gerla M, Internet of vehicles: From intelligent grid to autonomous cars and vehicular clouds. 2014 IEEE World Forum on Internet of Things (WF-IoT). 2014; 241–246.
  15. [15] Bansal P, et al. Assessing public opinions of and interest in new vehicle technologies: An Austin perspective. Transportation Research Part C: Emerging Technologies. 2016;67:1–14. DOI: 10.1016/j.trc.2016.01.019.
  16. [16] Panagiotopoulos I, Dimitrakopoulos G. An empirical investigation on consumers’ intentions towards autonomous driving. Transportation Research Part C: Emerging Technologies. 2018;95:773–784. DOI:10.1016/j.trc.2018.08.013.
  17. [17] Vaidya B, Mouftah HT. IoT applications and services for connected and autonomous electric vehicles. Arabian Journal for Science and Engineering. 2020;45(4):2559–2569. DOI: 10.1007/s13369-019-04216-8.
  18. [18] Luetge C. The German ethics code for automated and connected driving. Philosophy & Technology. 2017;30:547–558.
  19. [19] Ryan M. The future of transportation: Ethical, legal, social and economic impacts of self-driving vehicles in the Year 2025. Science and Engineering Ethics. 2020;26(3):1185–1208. DOI:10.1007/s11948-019-00130-2.
  20. [20] Liu N. Exploring expert perceptions about the cyber security and privacy of connected and autonomous vehicles: A thematic analysis approach. Transportation Research Part F: Traffic Psychology and Behaviour. 2020;75:66–86. DOI: 10.1016/j.trf.2020.09.019.
  21. [21] Merriman SE. Challenges for automated vehicle driver training: A thematic analysis from manual and automated driving. Transportation Research Part F: Traffic Psychology and Behaviour. 2021;76:238–268. DOI: 10.1016/j.trf.2020.10.011.
  22. [22] Kyriakidis M. Public opinion on automated driving: Results of an international questionnaire among 5000 respondents. Transportation Research Part F: Traffic Psychology and Behaviour. 2015;32:127–140. DOI:10.1016/j.trf.2015.04.014.
  23. [23] Saeed TU. An empirical discourse on forecasting the use of autonomous vehicles using consumers’ preferences. Technological Forecasting and Social Change. 2020;158:120130. DOI: 10.1016/j.techfore.2020.120130.
  24. [24] Wu J. Analysis of consumer attitudes towards autonomous, connected, and electric vehicles: A survey in China. Research in Transportation Economics. 2020;80:100828. DOI: 10.1016/j.retrec.2020.100828.
  25. [25] O’Brien SM, Dunson DB. Bayesian multivariate logistic regression. Biometrics. 2004;60:739–746. DOI:10.1111/j.0006-341X.2004.00224.x
  26. [26] Edara P, Chatterjee I. Multivariate regression for estimating driving behavior parameters in work zone simulation to replicate field capacities. Transportation Letters. 2010;2(3):175–186. DOI: 10.3328/TL.2010.02.03.175-186.
  27. [27] Hobert JP, et al. Convergence analysis of MCMC algorithms for Bayesian multivariate linear regression with non-Gaussian errors. Scandinavian Journal of Statistics. 2018;45:513–533. DOI:10.1111/sjos.12310.
Show more


Accelerating Discoveries in Traffic Science |
2024 © Promet - Traffic&Transportation journal