Modelling Passengers’ Travel Behaviour for Shared Autonomous Vehicle and Bus Considering Heterogeneity

Authors

  • Yaning WANG Inner Mongolia University, Transportation Institute
  • Yueying HUO Inner Mongolia University, Transportation Institute
  • Xuebin ZHANG Inner Mongolia University, Transportation Institute

DOI:

https://doi.org/10.7307/ptt.v36i4.506

Keywords:

shared autonomous vehicle, bus, travel behaviour, heterogeneity, mixed logit model

Abstract

The popularisation of autonomous vehicles will give rise to a new business model called shared autonomous vehicle (SAV). SAVs may attract a large number of passengers and lead to a decline in the share of buses, which can be interpreted by exploring passengers’ travel behaviour when confronting the SAV and bus modes. Thus, this paper addresses the SAV and bus passengers’ travel behaviour, aiming to examine the factors influencing travel behaviour and revealing the characteristics of SAV passengers. We classified passengers using latent class cluster analysis and modelled passengers’ travel behaviour based on confirmatory factor analysis and mixed logit model. The findings indicate a variation in travel preferences among different classes of travellers. Short-distance travellers pay less attention to travel time. Non-short-distance PT travellers are most likely to be affected by service attributes (waiting time, travel time and travel costs). Non-short-distance private car travellers are more likely to become early SAV adopters. Passengers travelling for short distances may be more likely to choose SAV, which reveals the potential of SAVs to become a first and last mile connection for public transport. Passengers lack trust in SAVs, which will affect their promotion.

References

Ackerman E. Hail, robo-taxi! [top tech 2017]. IEEE Spectrum. 2017;54:26-29. DOI: 10.1109/MSPEC.2017.7802740.

Rahimi A, et al. Potential implications of automated vehicle technologies on travel behavior: A literature review. International Conference on Transportation and Development 2020. 2020. p. 234-247. DOI: 10.1061/9780784483138.021.

Bischoff J, Maciejewski M. Simulation of city-wide replacement of private cars with autonomous taxis in Berlin. Procedia Computer Science. 2016;83:237-244. DOI: 10.1016/j.procs.2016.04.121.

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. Journal of Intelligent Transportation Systems. 2017;21:324-348. DOI: 10.1080/15472450.2017.1291351.

Gurumurthy KM, Kockelman KM. Analyzing the dynamic ride-sharing potential for shared autonomous vehicle fleets using cellphone data from Orlando, Florida. Computers, Environment and Urban Systems. 2018;71:177-185. DOI: 10.1016/j.compenvurbsys.2018.05.008.

Nazari F, Noruzoliaee M, Mohammadian AK. Shared versus private mobility: Modeling public interest in autonomous vehicles accounting for latent attitudes. Transportation Research Part C: Emerging Technologies. 2018;97:456-477. DOI: 10.1016/j.trc.2018.11.005.

Krueger R, Rashidi TH, Rose JM. Preferences for shared autonomous vehicles. Transportation Research Part C: Emerging Technologies. 2016;69:343-355. DOI: 10.1016/j.trc.2016.06.015.

Bösch PM, et al. Cost-based analysis of autonomous mobility services. Transport Policy. 2018;64:76-91. DOI: 10.1016/j.tranpol.2017.09.005.

Haboucha CJ, Ishaq R, Shiftan Y. User preferences regarding autonomous vehicles. Transportation Research Part C: Emerging Technologies. 2017;78:37-49. DOI: 10.1016/j.trc.2017.01.010.

Bansal P, Kockelman KM, Singh A. 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.

Maeng K, Cho Y. Who will want to use shared autonomous vehicle service and how much? A consumer experiment in South Korea. Travel Behaviour and Society. 2022;26:9-17. DOI: 10.1016/j.tbs.2021.08.001.

Rahimi A, et al. Adoption and willingness to pay for autonomous vehicles: attitudes and latent classes. Transportation research part D: Transport and Environment. 2020;89:102611. DOI: 10.1016/j.trd.2020.102611.

Etzioni S, et al. Preferences for shared automated vehicles: A hybrid latent class modeling approach. Transportation Research Part C: Emerging Technologies. 2021;125:103013. DOI: 10.1016/j.trc.2021.103013.

Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly. 1989:319-340. DOI: 10.2307/249008.

Han L, et al. The intention to adopt electric vehicles: Driven by functional and non-functional values. Transportation Research Part A: Policy and Practice. 2017;103:185-197. DOI: 10.1016/j.tra.2017.05.033.

Jaiswal D, Deshmukh AK, Thaichon P. Who will adopt electric vehicles? Segmenting and exemplifying potential buyer heterogeneity and forthcoming research. Journal of Retailing and Consumer Services. 2022;67:102969. DOI: 10.1016/j.jretconser.2022.102969.

Lanzini P, Khan SA. Shedding light on the psychological and behavioral determinants of travel mode choice: A meta-analysis. Transportation Research Part F: Traffic Psychology and Behaviour. 2017;48:13-27. DOI: 10.1016/j.trf.2017.04.020.

Wang S, et al. Policy implications for promoting the adoption of electric vehicles: do consumer’s knowledge, perceived risk and financial incentive policy matter? Transportation Research Part A: Policy and Practice. 2018;117:58-69. DOI: 10.1016/j.tra.2018.08.014.

Rahimi A, et al. Clustering approach toward large truck crash analysis. Transportation Research Record. 2019;2673:73-85. DOI: 10.1177/036119811983934.

Magidson J, Vermunt J. Latent class models for clustering: A comparison with K-means. Canadian Journal of Marketing Research. 2002;20:36-43. https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=6add265688cde63766bed6b920c4546e7c11ab99.

Train KE. Discrete choice methods with simulation. Cambridge University Press; 2009.

Hensher DA, et al. Applied choice analysis: a primer. Cambridge University Press; 2005.

Lubke G, Muthén BO. Performance of factor mixture models as a function of model size, covariate effects, and class-specific parameters. Structural Equation Modeling: A Multidisciplinary Journal. 2007;14:26-47. DOI: 10.1080/10705510709336735.

Shah R, Goldstein SM. Use of structural equation modeling in operations management research: Looking back and forward. Journal of Operations management. 2006;24:148-169. DOI: 10.1016/j.jom.2005.05.001.

Wang J, Wang X. Structural equation modeling: Applications using Mplus. John Wiley & Sons; 2019.

Raykov T. Estimation of composite reliability for congeneric measures. Applied Psychological Measurement. 1997;21:173-184. DOI: 10.1177/01466216970212006.

Ab Hamid M, Sami W, Sidek MM. Discriminant validity assessment: Use of Fornell & Larcker criterion versus HTMT criterion. Journal of Physics: Conference Series. 2017.p. 012163. DOI: 10.1088/1742-6596/890/1/012163.

Zhang T, et al. The roles of initial trust and perceived risk in public’s acceptance of automated vehicles. Transportation research part C: Emerging Technologies. 2019;98:207-220. DOI: 10.1016/j.trc.2018.11.018.

Liu J, et al. Tracking a system of shared autonomous vehicles across the Austin, Texas network using agent-based simulation. Transportation. 2017;44:1261-1278. DOI: 10.1007/s11116-017-9811-1.

Downloads

Published

27-08-2024

How to Cite

WANG, Y., HUO, Y., & ZHANG, X. (2024). Modelling Passengers’ Travel Behaviour for Shared Autonomous Vehicle and Bus Considering Heterogeneity. Promet - Traffic&Transportation, 36(4), 690–703. https://doi.org/10.7307/ptt.v36i4.506

Issue

Section

Articles