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Estimation method for railway passengers’ train choice behavior with smart card transaction data

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  • Takahiko Kusakabe
  • Takamasa Iryo
  • Yasuo Asakura

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  • 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.
    • Handle: RePEc:kap:transp:v:37:y:2010:i:5:p:731-749
    DOI: 10.1007/s11116-010-9290-0
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    References listed on IDEAS

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    1. Morency, Catherine & Trépanier, Martin & Agard, Bruno, 2007. "Measuring transit use variability with smart-card data," Transport Policy, Elsevier, vol. 14(3), pages 193-203, May.
    2. Bagchi, M. & White, P.R., 2005. "The potential of public transport smart card data," Transport Policy, Elsevier, vol. 12(5), pages 464-474, September.
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    Cited by:

    1. Hiroaki Nishiuchi & Yasuyuki Kobayashi & Tomoyuki Todoroki & Tomoya Kawasaki, 2018. "Impact analysis of reductions in tram services in rural areas in Japan using smart card data," Public Transport, Springer, vol. 10(2), pages 291-309, August.
    2. Junghan Baek & Keemin Sohn, 2016. "An investigation into passenger preference for express trains during peak hours," Transportation, Springer, vol. 43(4), pages 623-641, July.
    3. 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.
    4. Zhu, Yiwen & Koutsopoulos, Haris N. & Wilson, Nigel H.M., 2017. "A probabilistic Passenger-to-Train Assignment Model based on automated data," Transportation Research Part B: Methodological, Elsevier, vol. 104(C), pages 522-542.
    5. Roger Beecham & Jo Wood, 2014. "Exploring gendered cycling behaviours within a large-scale behavioural data-set," Transportation Planning and Technology, Taylor & Francis Journals, vol. 37(1), pages 83-97, February.
    6. Liu, Jiangtao & Zhou, Xuesong, 2019. "Observability quantification of public transportation systems with heterogeneous data sources: An information-space projection approach based on discretized space-time network flow models," Transportation Research Part B: Methodological, Elsevier, vol. 128(C), pages 302-323.
    7. Filip Covic & Stefan Voß, 2019. "Interoperable smart card data management in public mass transit," Public Transport, Springer, vol. 11(3), pages 523-548, October.
    8. Yanshuo Sun & Jungang Shi & Paul M. Schonfeld, 2016. "Identifying passenger flow characteristics and evaluating travel time reliability by visualizing AFC data: a case study of Shanghai Metro," Public Transport, Springer, vol. 8(3), pages 341-363, December.
    9. Weiyan Mu & Xin Wang & Chunya Li & Shifeng Xiong, 2023. "Dynamic Modeling for Metro Passenger Flows on Congested Transfer Routes," Mathematics, MDPI, vol. 11(6), pages 1-20, March.
    10. Neema Nassir & Mark Hickman & Zhen-Liang Ma, 2015. "Activity detection and transfer identification for public transit fare card data," Transportation, Springer, vol. 42(4), pages 683-705, July.
    11. Hong En Tan & De Wen Soh & Yong Sheng Soh & Muhamad Azfar Ramli, 2021. "Derivation of train arrival timings through correlations from individual passenger farecard data," Transportation, Springer, vol. 48(6), pages 3181-3205, December.
    12. 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.
    13. Toru Seo & Kentaro Wada & Daisuke Fukuda, 2023. "Fundamental diagram of urban rail transit considering train–passenger interaction," Transportation, Springer, vol. 50(4), pages 1399-1424, August.
    14. 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.
    15. Taoyuan Yang & Peng Zhao & Xiangming Yao, 2020. "A Method to Estimate URT Passenger Spatial-Temporal Trajectory with Smart Card Data and Train Schedules," Sustainability, MDPI, vol. 12(6), pages 1-13, March.
    16. Xing Chen & Leishan Zhou & Yixiang Yue & Yu Zhou & Liwen Liu, 2018. "Data-Driven Method to Estimate the Maximum Likelihood Space–Time Trajectory in an Urban Rail Transit System," Sustainability, MDPI, vol. 10(6), pages 1-21, May.
    17. Yi Zhu, 2020. "Estimating the activity types of transit travelers using smart card transaction data: a case study of Singapore," Transportation, Springer, vol. 47(6), pages 2703-2730, December.
    18. Hörcher, Daniel & Graham, Daniel J. & Anderson, Richard J., 2017. "Crowding cost estimation with large scale smart card and vehicle location data," Transportation Research Part B: Methodological, Elsevier, vol. 95(C), pages 105-125.
    19. Zhichao Cao & Zhenzhou Yuan & Silin Zhang, 2016. "Performance Analysis of Stop-Skipping Scheduling Plans in Rail Transit under Time-Dependent Demand," IJERPH, MDPI, vol. 13(7), pages 1-23, July.
    20. Hänseler, Flurin S. & van den Heuvel, Jeroen P.A. & Cats, Oded & Daamen, Winnie & Hoogendoorn, Serge P., 2020. "A passenger-pedestrian model to assess platform and train usage from automated data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 948-968.
    21. Ying Song & Yingling Fan & Xin Li & Yanjie Ji, 2018. "Multidimensional visualization of transit smartcard data using space–time plots and data cubes," Transportation, Springer, vol. 45(2), pages 311-333, March.
    22. De Zhao & Wei Wang & Amber Woodburn & Megan S. Ryerson, 2017. "Isolating high-priority metro and feeder bus transfers using smart card data," Transportation, Springer, vol. 44(6), pages 1535-1554, November.
    23. Wu, Laiyun & Kang, Jee Eun & Chung, Younshik & Nikolaev, Alexander, 2021. "Inferring origin-Destination demand and user preferences in a multi-modal travel environment using automated fare collection data," Omega, Elsevier, vol. 101(C).

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