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Understanding passenger route choice behavior under the influence of detailed route information based on smart card data

Author

Listed:
  • Zhuangbin Shi

    (Kunming University of Science and Technology)

  • Wenqin Pan

    (Kunming University of Science and Technology)

  • Mingwei He

    (Kunming University of Science and Technology)

  • Yang Liu

    (Kunming University of Science and Technology)

Abstract

Most previous studies explored the route choice behavior of metro passengers using stated preference (SP) survey data, but the SP data are inevitably subject to endogenous and selection bias. In contrast, automated fare collection (AFC) data record travel information for nearly all passengers at boarding and alighting stations. However, due to the seamless transfer in urban rail transit, it becomes challenging to track the actual routes of passengers accurately using AFC data. Fortunately, based on a data-driven method, the chosen route and detailed travel information (e.g., segmented travel time, train load status) can be inferred with AFC data. To fill the research gaps, this paper delves into the route choice mechanism by considering the effect of detailed route information, taking Nanjing Metro, China as a case study. A Conditional Multinomial Logit model is employed to examine the effect of determinants on route choice behavior for metro passengers. The results show that the route choice model considering dynamic segmented travel time and train load status has better fit performance than the benchmark models. The sensitivity of the walking time is found to be similar to that of in-vehicle time for metro passengers, but a stronger distaste for waiting time or queuing time is observed. Besides, the crowding-related attributes are negative for route choice, but Nanjing Metro passengers present a higher tolerance for crowding compared with passengers in developed countries. These findings provide an accurate and comprehensive insight into the route choice behavior of metro passengers.

Suggested Citation

  • Zhuangbin Shi & Wenqin Pan & Mingwei He & Yang Liu, 2025. "Understanding passenger route choice behavior under the influence of detailed route information based on smart card data," Transportation, Springer, vol. 52(2), pages 615-639, April.
    • Handle: RePEc:kap:transp:v:52:y:2025:i:2:d:10.1007_s11116-023-10432-x
    DOI: 10.1007/s11116-023-10432-x
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    References listed on IDEAS

    as
    1. Buckell, John & White, Justin S. & Shang, Ce, 2020. "Can incentive-compatibility reduce hypothetical bias in smokers’ experimental choice behavior? A randomized discrete choice experiment," Journal of choice modelling, Elsevier, vol. 37(C).
    2. Kim, Kyung Min & Hong, Sung-Pil & Ko, Suk-Joon & Kim, Dowon, 2015. "Does crowding affect the path choice of metro passengers?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 77(C), pages 292-304.
    3. Luan, Xiaojie & Corman, Francesco, 2022. "Passenger-oriented traffic control for rail networks: An optimization model considering crowding effects on passenger choices and train operations," Transportation Research Part B: Methodological, Elsevier, vol. 158(C), pages 239-272.
    4. Liu, Yang & Ji, Yanjie & Shi, Zhuangbin & He, Baohong & Liu, Qiyang, 2018. "Investigating the effect of the spatial relationship between home, workplace and school on parental chauffeurs’ daily travel mode choice," Transport Policy, Elsevier, vol. 69(C), pages 78-87.
    5. Sung-Pil Hong & Yun-Hong Min & Myoung-Ju Park & Kyung Min Kim & Suk Mun Oh, 2016. "Precise estimation of connections of metro passengers from Smart Card data," Transportation, Springer, vol. 43(5), pages 749-769, September.
    6. Tao, Sui & Rohde, David & Corcoran, Jonathan, 2014. "Examining the spatial–temporal dynamics of bus passenger travel behaviour using smart card data and the flow-comap," Journal of Transport Geography, Elsevier, vol. 41(C), pages 21-36.
    7. Tomhave, Benjamin J. & Khani, Alireza, 2022. "Refined choice set generation and the investigation of multi-criteria transit route choice behavior," Transportation Research Part A: Policy and Practice, Elsevier, vol. 155(C), pages 484-500.
    8. Wu, Jianjun & Qu, Yunchao & Sun, Huijun & Yin, Haodong & Yan, Xiaoyong & Zhao, Jiandong, 2019. "Data-driven model for passenger route choice in urban metro network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 787-798.
    9. Krčál, Ondřej & Peer, Stefanie & Staněk, Rostislav & Karlínová, Bára, 2019. "Real consequences matter: Why hypothetical biases in the valuation of time persist even in controlled lab experiments," Economics of Transportation, Elsevier, vol. 20(C).
    10. Ye, Xin & Garikapati, Venu M. & You, Daehyun & Pendyala, Ram M., 2017. "A practical method to test the validity of the standard Gumbel distribution in logit-based multinomial choice models of travel behavior," Transportation Research Part B: Methodological, Elsevier, vol. 106(C), pages 173-192.
    11. Ikki Kim & Hyoung-Chul Kim & Dong-Jeong Seo & Jung In Kim, 2020. "Calibration of a transit route choice model using revealed population data of smartcard in a multimodal transit network," Transportation, Springer, vol. 47(5), pages 2179-2202, October.
    12. Tirachini, Alejandro & Hurtubia, Ricardo & Dekker, Thijs & Daziano, Ricardo A., 2017. "Estimation of crowding discomfort in public transport: Results from Santiago de Chile," Transportation Research Part A: Policy and Practice, Elsevier, vol. 103(C), pages 311-326.
    13. Takahiko Kusakabe & Takamasa Iryo & Yasuo Asakura, 2010. "Estimation method for railway passengers’ train choice behavior with smart card transaction data," Transportation, Springer, vol. 37(5), pages 731-749, September.
    14. Raveau, Sebastián & Muñoz, Juan Carlos & de Grange, Louis, 2011. "A topological route choice model for metro," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(2), pages 138-147, February.
    15. Liu, Yang & Feng, Tao & Shi, Zhuangbin & He, Mingwei, 2022. "Understanding the route choice behaviour of metro-bikeshare users," Transportation Research Part A: Policy and Practice, Elsevier, vol. 166(C), pages 460-475.
    16. Mark Wardman & Gerard Whelan, 2011. "Twenty Years of Rail Crowding Valuation Studies: Evidence and Lessons from British Experience," Transport Reviews, Taylor & Francis Journals, vol. 31(3), pages 379-398.
    17. Tirachini, Alejandro & Sun, Lijun & Erath, Alexander & Chakirov, Artem, 2016. "Valuation of sitting and standing in metro trains using revealed preferences," Transport Policy, Elsevier, vol. 47(C), pages 94-104.
    18. Hironori Kato & Yuichiro Kaneko & Masashi Inoue, 2010. "Comparative analysis of transit assignment: evidence from urban railway system in the Tokyo Metropolitan Area," Transportation, Springer, vol. 37(5), pages 775-799, September.
    19. Yu, Chao & Li, Haiying & Xu, Xinyue & Liu, Jun, 2020. "Data-driven approach for solving the route choice problem with traveling backward behavior in congested metro systems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 142(C).
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