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Data-driven model for passenger route choice in urban metro network

Author

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  • Wu, Jianjun
  • Qu, Yunchao
  • Sun, Huijun
  • Yin, Haodong
  • Yan, Xiaoyong
  • Zhao, Jiandong

Abstract

Passenger flow distribution in the metro system is fundamental for many applications such as network planning and design, passenger flow forecasting, individual travel activity modeling and emergency response management. However, in most metro systems the smart card automated fare collection (AFC) equipment in Beijing only record when and where a passenger enters and leaves the metro network. Therefore, how to accurately determine passenger flow distribution in unknown travel routes remains a challenging task for the managers. This paper presents a methodology for reconstructing metro passenger flow distribution from large-scale smart card data. A clustering method was first applied to group the travel time of passengers between origin–destination (OD) station pairs into different clusters. Then an approach was proposed that considered both uncertain walking time and transfer time, to estimate the theoretical travel time of all possible routes between the OD pair. An approach to measure the similarity was further employed to match each travel time cluster to a most-likely travel route, and finally obtained the passengers’ flow of every route. Compared with two classical methods, the proposed approach was more accurate and efficient.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:phsmap:v:524:y:2019:i:c:p:787-798
    DOI: 10.1016/j.physa.2019.04.231
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    References listed on IDEAS

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    Cited by:

    1. Yu, Liping & Liu, Huiran & Fang, Zhiming & Ye, Rui & Huang, Zhongyi & You, Yayun, 2023. "A new approach on passenger flow assignment with multi-connected agents," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 628(C).
    2. Yang, Hongtai & Ping, An & Wei, Hongmin & Zhai, Guocong, 2023. "Unique in the metro system: The likelihood to re-identify a metro user with limited trajectory points," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 628(C).
    3. Ding, Heng & Di, Yunran & Zheng, Xiaoyan & Liu, Kai & Zhang, Weihua & Zheng, Lingling, 2021. "Passenger arrival distribution model and riding guidance on an urban rail transit platform," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 571(C).
    4. Cortés, Cristián E. & Donoso, Pedro & Gutiérrez, Leonel & Herl, Daniel & Muñoz, Diego, 2023. "A recursive stochastic transit equilibrium model estimated using passive data from Santiago, Chile," Transportation Research Part B: Methodological, Elsevier, vol. 174(C).
    5. Shang, Pan & Xiong, Yufan & Guo, Jifu & Xian, Kai & Yu, Yun & Xu, Han, 2024. "A modeling framework to integrate frequency - and schedule-based passenger assignment approaches for coordinated path choice and space-time trajectory estimation based on multi-source observations," Transportation Research Part B: Methodological, Elsevier, vol. 183(C).
    6. 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).
    7. Jia, Jianlin & Chen, Yanyan & Wang, Yang & Li, Tongfei & Li, Yongxing, 2021. "A new global method for identifying urban rail transit key station during COVID-19: A case study of Beijing, China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    8. Guo, Xin & Wang, David Z.W. & Wu, Jianjun & Sun, Huijun & Zhou, Li, 2020. "Mining commuting behavior of urban rail transit network by using association rules," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 559(C).

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