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A strategy-based recursive path choice model for public transit smart card data

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  • Nassir, Neema
  • Hickman, Mark
  • Ma, Zhen-Liang

Abstract

A recursive logit model is proposed for path choice modeling with transit smart card data in higher-frequency bus and rail services. In such circumstances, it is commonly assumed that passengers may arrive randomly to a stop and may behave according to a “strategy”; such a strategy is associated with a so-called “attractive” set of routes: a passenger selects a set of routes departing from the stop, and will board the next vehicle to depart from among that set of routes. We extend the conventional notion of attractive sets by introducing a measure of “attractiveness” that allows for randomness in the choice of attractive routes.

Suggested Citation

  • Nassir, Neema & Hickman, Mark & Ma, Zhen-Liang, 2019. "A strategy-based recursive path choice model for public transit smart card data," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 528-548.
    • Handle: RePEc:eee:transb:v:126:y:2019:i:c:p:528-548
    DOI: 10.1016/j.trb.2018.01.002
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    4. Mepparambath, Rakhi Manohar & Soh, Yong Sheng & Jayaraman, Vasundhara & Tan, Hong En & Ramli, Muhamad Azfar, 2023. "A novel modelling approach of integrated taxi and transit mode and route choice using city-scale emerging mobility data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 170(C).
    5. Amir Ghorbani & Vahid Ghorbani & Morteza Nazari-Heris & Somayeh Asadi, 2023. "Data Assimilation for Agent-Based Models," Mathematics, MDPI, vol. 11(20), pages 1-25, October.
    6. 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.
    7. Mai, Tien & Yu, Xinlian & Gao, Song & Frejinger, Emma, 2021. "Routing policy choice prediction in a stochastic network: Recursive model and solution algorithm," Transportation Research Part B: Methodological, Elsevier, vol. 151(C), pages 42-58.
    8. Zhang, Pujun & Lei, Dazhou & Liu, Shan & Jiang, Hai, 2024. "Recursive logit-based meta-inverse reinforcement learning for driver-preferred route planning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 185(C).
    9. Nielsen, Otto Anker & Eltved, Morten & Anderson, Marie Karen & Prato, Carlo Giacomo, 2021. "Relevance of detailed transfer attributes in large-scale multimodal route choice models for metropolitan public transport passengers," Transportation Research Part A: Policy and Practice, Elsevier, vol. 147(C), pages 76-92.

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