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Exploring behavioral heterogeneities of metro passenger’s travel plan choice under unplanned service disruption with uncertainty

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  • Li, Binbin
  • Yao, Enjian
  • Yamamoto, Toshiyuki
  • Tang, Ying
  • Liu, Shasha

Abstract

Understanding metro passenger’s travel plan choice behavior under unplanned service disruptions is vital for transit agencies. It allows capturing the changes in the demands of the passengers, adopting measures aimed at minimizing the impact on the transit system, and ensuring the safety of the interrupted passengers. Different from planned metro service disruptions, unplanned service disruptions cannot be known in advance and have high uncertainty. However, little is known regarding the role of the uncertain duration in the decision-making process and the taste heterogeneity in the behavior under unplanned metro service disruptions. To fill this gap, we first established a time interval in each scenario of a stated preference questionnaire to indicate the uncertain duration and conducted a web-based survey. Based on the survey data collected at Guangzhou, China, we developed an error component latent class model for travel plan choice behavior considering uncertainty and heterogeneity. The model result showed that the population can be classified into two classes, uncertainty pessimists and uncertainty optimists, who have a strong and weak perception of the uncertainty of the disruption duration, respectively, based on socio-demographic and travel characteristic attributes. Concurrently, the perception of uncertainty in both the classes is enhanced during peak hours and when a passenger enters a station. The findings of this study provide more insights into passenger’s travel behavior under unplanned service disruptions with uncertainty. Moreover, they can assist transit agencies in adopting effective management strategies, which, in turn, will aid in improving the service quality for its passengers.

Suggested Citation

  • Li, Binbin & Yao, Enjian & Yamamoto, Toshiyuki & Tang, Ying & Liu, Shasha, 2020. "Exploring behavioral heterogeneities of metro passenger’s travel plan choice under unplanned service disruption with uncertainty," Transportation Research Part A: Policy and Practice, Elsevier, vol. 141(C), pages 294-306.
    • Handle: RePEc:eee:transa:v:141:y:2020:i:c:p:294-306
    DOI: 10.1016/j.tra.2020.09.009
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    1. Brendan Pender & Graham Currie & Alexa Delbosc & Nirajan Shiwakoti, 2014. "Social Media Use during Unplanned Transit Network Disruptions: A Review of Literature," Transport Reviews, Taylor & Francis Journals, vol. 34(4), pages 501-521, July.
    2. Caussade, Sebastián & Ortúzar, Juan de Dios & Rizzi, Luis I. & Hensher, David A., 2005. "Assessing the influence of design dimensions on stated choice experiment estimates," Transportation Research Part B: Methodological, Elsevier, vol. 39(7), pages 621-640, August.
    3. Sun, Daniel (Jian) & Guan, Shituo, 2016. "Measuring vulnerability of urban metro network from line operation perspective," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 348-359.
    4. Fredrik Carlsson, 2003. "The demand for intercity public transport: the case of business passengers," Applied Economics, Taylor & Francis Journals, vol. 35(1), pages 41-50.
    5. Sarker, Rumana Islam & Kaplan, Sigal & Mailer, Markus & Timmermans, Harry J.P., 2019. "Applying affective event theory to explain transit users’ reactions to service disruptions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 593-605.
    6. Stephane Hess, 2014. "Latent class structures: taste heterogeneity and beyond," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 14, pages 311-330, Edward Elgar Publishing.
    7. Sun, Daniel(Jian) & Ding, Xueqing, 2019. "Spatiotemporal evolution of ridesourcing markets under the new restriction policy: A case study in Shanghai," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 227-239.
    8. Sun, Huijun & Wu, Jianjun & Wu, Lijuan & Yan, Xiaoyong & Gao, Ziyou, 2016. "Estimating the influence of common disruptions on urban rail transit networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 62-75.
    9. van Exel, N.J.A. & Rietveld, P., 2009. "When strike comes to town... anticipated and actual behavioural reactions to a one-day, pre-announced, complete rail strike in the Netherlands," Transportation Research Part A: Policy and Practice, Elsevier, vol. 43(5), pages 526-535, June.
    10. Anastasia Pnevmatikou & Matthew Karlaftis & Konstantinos Kepaptsoglou, 2015. "Metro service disruptions: how do people choose to travel?," Transportation, Springer, vol. 42(6), pages 933-949, November.
    11. Junyi Shen, 2009. "Latent class model or mixed logit model? A comparison by transport mode choice data," Applied Economics, Taylor & Francis Journals, vol. 41(22), pages 2915-2924.
    12. Saadatseresht, Mohammad & Mansourian, Ali & Taleai, Mohammad, 2009. "Evacuation planning using multiobjective evolutionary optimization approach," European Journal of Operational Research, Elsevier, vol. 198(1), pages 305-314, October.
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