IDEAS home Printed from https://ideas.repec.org/a/eee/transa/v141y2020icp294-306.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0965856420307126
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.tra.2020.09.009?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chengli Cong & Xuan Li & Shiwei Yang & Quan Zhang & Lili Lu & Yang Shi, 2022. "Impact Estimation of Unplanned Urban Rail Disruptions on Public Transport Passengers: A Multi-Agent Based Simulation Approach," IJERPH, MDPI, vol. 19(15), pages 1-25, July.
    2. Kim, Sung Hoo & Mokhtarian, Patricia L., 2023. "Finite mixture (or latent class) modeling in transportation: Trends, usage, potential, and future directions," Transportation Research Part B: Methodological, Elsevier, vol. 172(C), pages 134-173.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Elisa Borowski & Jason Soria & Joseph Schofer & Amanda Stathopoulos, 2020. "Disparities in ridesourcing demand for mobility resilience: A multilevel analysis of neighborhood effects in Chicago, Illinois," Papers 2010.15889, arXiv.org.
    2. 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.
    3. Nguyen-Phuoc, Duy Q. & Currie, Graham & De Gruyter, Chris & Young, William, 2018. "Transit user reactions to major service withdrawal – A behavioural study," Transport Policy, Elsevier, vol. 64(C), pages 29-37.
    4. José Grisolía & Kenneth Willis, 2012. "A latent class model of theatre demand," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 36(2), pages 113-139, May.
    5. Mikołaj Czajkowski & Nick Hanley & Jacob LaRiviere, 2016. "Controlling for the Effects of Information in a Public Goods Discrete Choice Model," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 63(3), pages 523-544, March.
    6. Joshua Auld & Hubert Ley & Omer Verbas & Nima Golshani & Josiane Bechara & Angela Fontes, 2020. "A stated-preference intercept survey of transit-rider response to service disruptions," Public Transport, Springer, vol. 12(3), pages 557-585, October.
    7. Lin, Teddy & Shalaby, Amer & Miller, Eric, 2016. "Transit User Behaviour in Response to Service Disruption: State of Knowledge," 57th Transportation Research Forum (51st CTRF) Joint Conference, Toronto, Ontario, May 1-4, 2016 319263, Transportation Research Forum.
    8. Chengli Cong & Xuan Li & Shiwei Yang & Quan Zhang & Lili Lu & Yang Shi, 2022. "Impact Estimation of Unplanned Urban Rail Disruptions on Public Transport Passengers: A Multi-Agent Based Simulation Approach," IJERPH, MDPI, vol. 19(15), pages 1-25, July.
    9. Spyropoulou, Ioanna, 2020. "Impact of public transport strikes on the road network: The case of Athens," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 651-665.
    10. Sarrias, Mauricio, 2021. "A two recursive equation model to correct for endogeneity in latent class binary probit models," Journal of choice modelling, Elsevier, vol. 40(C).
    11. Zheng, Shuai & Liu, Yugang & Lin, Yexin & Wang, Qiang & Yang, Hongtai & Chen, Bin, 2022. "Bridging strategy for the disruption of metro considering the reliability of transportation system: Metro and conventional bus network," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    12. Singh, Jyotsna & Homem de Almeida Correia, Gonçalo & van Wee, Bert & Barbour, Natalia, 2023. "Change in departure time for a train trip to avoid crowding during the COVID-19 pandemic: A latent class study in the Netherlands," Transportation Research Part A: Policy and Practice, Elsevier, vol. 170(C).
    13. Parkes, Stephen D. & Jopson, Ann & Marsden, Greg, 2016. "Understanding travel behaviour change during mega-events: Lessons from the London 2012 Games," Transportation Research Part A: Policy and Practice, Elsevier, vol. 92(C), pages 104-119.
    14. Zhang, Liye & Xiao, Zhe & Ren, Shen & Qin, Zheng & Goh, Rick Siow Mong & Song, Jie, 2022. "Measuring the vulnerability of bike-sharing system," Transportation Research Part A: Policy and Practice, Elsevier, vol. 163(C), pages 353-369.
    15. Jianhua Zhang & Ziqi Wang & Shuliang Wang & Shengyang Luan & Wenchao Shao, 2020. "Vulnerability Assessments of Urban Rail Transit Networks Based on Redundant Recovery," Sustainability, MDPI, vol. 12(14), pages 1-14, July.
    16. Jia, Wenjian & Chen, T. Donna, 2023. "Investigating heterogeneous preferences for plug-in electric vehicles: Policy implications from different choice models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 173(C).
    17. repec:sss:wpaper:201404 is not listed on IDEAS
    18. Rodríguez-Entrena, Macario & Espinosa-Goded, María & Barreiro-Hurlé, Jesús, 2014. "The role of ancillary benefits on the value of agricultural soils carbon sequestration programmes: Evidence from a latent class approach to Andalusian olive groves," Ecological Economics, Elsevier, vol. 99(C), pages 63-73.
    19. Fowri, Hamid R. & Seyedabrishami, Seyedehsan, 2020. "Assessment of urban transportation pricing policies with incorporation of unobserved heterogeneity," Transport Policy, Elsevier, vol. 99(C), pages 12-19.
    20. Nima Golshani & Ehsan Rahimi & Ramin Shabanpour & Kouros Mohammadian & Joshua Auld & Hubert Ley, 2020. "Passengers' Travel Behavior in Response to Unplanned Transit Disruptions," Papers 2001.01718, arXiv.org, revised Jul 2020.
    21. Sarrias, Mauricio & Daziano, Ricardo A., 2018. "Individual-specific point and interval conditional estimates of latent class logit parameters," Journal of choice modelling, Elsevier, vol. 27(C), pages 50-61.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:transa:v:141:y:2020:i:c:p:294-306. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/547/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.