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A new approach on passenger flow assignment with multi-connected agents

Author

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  • Yu, Liping
  • Liu, Huiran
  • Fang, Zhiming
  • Ye, Rui
  • Huang, Zhongyi
  • You, Yayun

Abstract

Efficient passenger flow assignment is essential for ensuring the safe and stable operation of the urban rail transit (URT). This paper proposes a new passenger flow assignment approach to address this challenge. The approach generates two agents: connected vehicle and unconnected passenger, to simulate vehicle operation and passenger travel for the URT network. It derives the passenger trajectory by the passenger route evolution mechanism to achieve the passenger flow assignment for the whole network. Furthermore, it provides statistics on platform passenger flow when obtaining the overall station passenger flow. Meanwhile, the approach employed Automatic Fare Collection (AFC) data as input to analyze the passenger flow features of the Shanghai Metro. The passenger flow assignment results can reveal the start and end of morning and evening peak hours at a time granularity of seconds. That is, 7:16:20 and 9:33:00 for the morning peak and 16:49:00 and 19:22:50 for the evening peak, respectively. The results also reveal the route choice pattern of passengers. Passengers have preferences for each type of route, in descending order: the shortest route, the minimum number of stations route, and the minimum number of transfer stations route, when they have a choice of these three routes. Moreover, the results can accurately identify large passenger flow stations and provide data support for station passenger flow management strategies.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:phsmap:v:628:y:2023:i:c:s0378437123007306
    DOI: 10.1016/j.physa.2023.129175
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    References listed on IDEAS

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    1. 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.
    2. Xie, J. & Wong, S.C. & Zhan, S. & Lo, S.M. & Chen, Anthony, 2020. "Train schedule optimization based on schedule-based stochastic passenger assignment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 136(C).
    3. Zhou, Yu & Yang, Hai & Wang, Yun & Yan, Xuedong, 2021. "Integrated line configuration and frequency determination with passenger path assignment in urban rail transit networks," Transportation Research Part B: Methodological, Elsevier, vol. 145(C), pages 134-151.
    4. Schmöcker, Jan-Dirk & Bell, Michael G.H. & Kurauchi, Fumitaka, 2008. "A quasi-dynamic capacity constrained frequency-based transit assignment model," Transportation Research Part B: Methodological, Elsevier, vol. 42(10), pages 925-945, December.
    5. 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.
    6. 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.
    7. Shang, Pan & Li, Ruimin & Guo, Jifu & Xian, Kai & Zhou, Xuesong, 2019. "Integrating Lagrangian and Eulerian observations for passenger flow state estimation in an urban rail transit network: A space-time-state hyper network-based assignment approach," Transportation Research Part B: Methodological, Elsevier, vol. 121(C), pages 135-167.
    8. Lianbo Deng & Junhao Zeng & Hongda Mei, 2019. "Passenger Flow Pushing Assignment Method for an Urban Rail Network Based on Hierarchical Path and Line Decomposition," Sustainability, MDPI, vol. 11(22), pages 1-21, November.
    9. Mo, Baichuan & Koutsopoulos, Haris N. & Zhao, Jinhua, 2022. "Inferring passenger responses to urban rail disruptions using smart card data: A probabilistic framework," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 159(C).
    10. 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.
    11. Kai Lu & Nan Cao & Jing-Hu Pan, 2022. "Passenger Behavior Simulation in Congested Urban Rail Transit System: A Capacity-Limited Optimal Strategy Model for Passenger Assignment," Complexity, Hindawi, vol. 2022, pages 1-13, January.
    12. 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).
    13. Agostino Nuzzolo & Francesco Russo & Umberto Crisalli, 2001. "A Doubly Dynamic Schedule-based Assignment Model for Transit Networks," Transportation Science, INFORMS, vol. 35(3), pages 268-285, August.
    14. Xia, Dawen & Jiang, Shunying & Yang, Nan & Hu, Yang & Li, Yantao & Li, Huaqing & Wang, Lin, 2021. "Discovering spatiotemporal characteristics of passenger travel with mobile trajectory big data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 578(C).
    15. Zhou, Yuyang & Zheng, Shuyan & Hu, Zhonghui & Chen, Yanyan, 2022. "Metro station risk classification based on smart card data: A case study in Beijing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 594(C).
    16. Sang Nguyen & Stefano Pallottino & Federico Malucelli, 2001. "A Modeling Framework for Passenger Assignment on a Transport Network with Timetables," Transportation Science, INFORMS, vol. 35(3), pages 238-249, August.
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