IDEAS home Printed from https://ideas.repec.org/a/wly/complx/v2022y2022i1n5451017.html

Data‐Driven Method for Passenger Path Choice Inference in Congested Subway Network

Author

Listed:
  • Guanghui Su
  • Bingfeng Si
  • Fang Zhao
  • He Li

Abstract

In a congested large‐scale subway network, the distribution of passenger flow in space‐time dimension is very complex. Accurate estimation of passenger path choice is very important to understand the passenger flow distribution and even improve the operation service level. The availability of automated fare collection (AFC) data, timetable, and network topology data opens up a new opportunity to study this topic based on multisource data. A probability model is proposed in this study to calculate the individual passenger’s path choice with multisource data, in which the impact of the network time‐varying state (e.g., path travel time) on passenger path choice is fully considered. First, according to the number and characteristics of OD (origin‐destination) candidate paths, the AFC data among special kinds of OD are selected to estimate the distribution of passengers’ walking time and waiting time of each platform. Then, based on the composition of path travel time, its real‐time probability distribution is formulated with the distribution of walking time, waiting time, and in‐vehicle time as parameters. Finally, a membership function is introduced to evaluate the dependence between passenger’s travel time and the real‐time travel time distribution of each candidate path and take the path with the largest membership degree as passenger’s choice. Finally, a case study with Beijing Subway data is applied to verify the effectiveness of the model presented in this study. We have compared and analysed the path calculation results in which the time‐varying characteristics of network state are considered or not. The results indicate that a passenger’s path choice behavior is affected by the network time‐varying state, and our model can quantify the time‐varying state and its impact on passenger path choice.

Suggested Citation

  • Guanghui Su & Bingfeng Si & Fang Zhao & He Li, 2022. "Data‐Driven Method for Passenger Path Choice Inference in Congested Subway Network," Complexity, John Wiley & Sons, vol. 2022(1).
    • Handle: RePEc:wly:complx:v:2022:y:2022:i:1:n:5451017
    DOI: 10.1155/2022/5451017
    as

    Download full text from publisher

    File URL: https://doi.org/10.1155/2022/5451017
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2022/5451017?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Wen-Long Shang & Yanyan Chen & Huibo Bi & Haoran Zhang & Changxi Ma & Washington Y. Ochieng, 2020. "Statistical Characteristics and Community Analysis of Urban Road Networks," Complexity, Hindawi, vol. 2020, pages 1-21, September.
    2. Bi, Huibo & Shang, Wen-Long & Chen, Yanyan & Wang, Kezhi & Yu, Qing & Sui, Yi, 2021. "GIS aided sustainable urban road management with a unifying queueing and neural network model," Applied Energy, Elsevier, vol. 291(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yu, Qing & Li, Weifeng & Zhang, Haoran & Chen, Jinyu, 2022. "GPS data in taxi-sharing system: Analysis of potential demand and assessment of fuel consumption based on routing probability model," Applied Energy, Elsevier, vol. 314(C).
    2. Zhou, Junfeng & Zhang, Yanhui & Zhang, Yubo & Shang, Wen-Long & Yang, Zhile & Feng, Wei, 2022. "Parameters identification of photovoltaic models using a differential evolution algorithm based on elite and obsolete dynamic learning," Applied Energy, Elsevier, vol. 314(C).
    3. Chong Yin & Yingxin Cui & Yue Liu & Xiaoni Su, 2022. "Construction and Structural Analysis of Inter‐Regional Industrial Circular Network: A Case of the Middle and Lower Reaches of the Yellow River in China," Complexity, John Wiley & Sons, vol. 2022(1).
    4. Xing Li & Fuzhou Luo, 2022. "Dynamic Measurement Analysis of Urban Innovation Ability and Ecological Efficiency in China," Complexity, John Wiley & Sons, vol. 2022(1).
    5. Dai, Rongjian & Ding, Chuan & Gao, Jian & Wu, Xinkai & Yu, Bin, 2022. "Optimization and evaluation for autonomous taxi ride-sharing schedule and depot location from the perspective of energy consumption," Applied Energy, Elsevier, vol. 308(C).
    6. Chen, Haoqian & Sui, Yi & Shang, Wen-long & Sun, Rencheng & Chen, Zhiheng & Wang, Changying & Han, Chunjia & Zhang, Yuqian & Zhang, Haoran, 2022. "Towards renewable public transport: Mining the performance of electric buses using solar-radiation as an auxiliary power source," Applied Energy, Elsevier, vol. 325(C).
    7. Qiu, Dawei & Wang, Yi & Sun, Mingyang & Strbac, Goran, 2022. "Multi-service provision for electric vehicles in power-transportation networks towards a low-carbon transition: A hierarchical and hybrid multi-agent reinforcement learning approach," Applied Energy, Elsevier, vol. 313(C).
    8. Wenjing Wang & Yanyan Chen & Haodong Sun & Yusen Chen, 2021. "Multiple Binary Classification Model of Trip Chain Based on the Fusion of Internet Location Data and Transport Data," Sustainability, MDPI, vol. 13(21), pages 1-15, November.
    9. Pi, Dawei & Xue, Pengyu & Wang, Weihua & Xie, Boyuan & Wang, Hongliang & Wang, Xianhui & Yin, Guodong, 2023. "Automotive platoon energy-saving: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 179(C).
    10. Zhao, Shihao & Li, Kang & Yang, Zhile & Xu, Xinzhi & Zhang, Ning, 2022. "A new power system active rescheduling method considering the dispatchable plug-in electric vehicles and intermittent renewable energies," Applied Energy, Elsevier, vol. 314(C).
    11. repec:osf:socarx:tk93y_v1 is not listed on IDEAS
    12. Qiao, Dongdong & Wei, Xuezhe & Fan, Wenjun & Jiang, Bo & Lai, Xin & Zheng, Yuejiu & Tang, Xiaolin & Dai, Haifeng, 2022. "Toward safe carbon–neutral transportation: Battery internal short circuit diagnosis based on cloud data for electric vehicles," Applied Energy, Elsevier, vol. 317(C).
    13. Shang, Wen-Long & Zhang, Mengxiao & Wu, Guoyuan & Yang, Lan & Fang, Shan & Ochieng, Washington, 2023. "Estimation of traffic energy consumption based on macro-micro modelling with sparse data from Connected and Automated Vehicles," Applied Energy, Elsevier, vol. 351(C).
    14. Fescioglu-Unver, Nilgun & Yıldız Aktaş, Melike, 2023. "Electric vehicle charging service operations: A review of machine learning applications for infrastructure planning, control, pricing and routing," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    15. Xuanwei Zhao & Jinsong Han, 2025. "How Is Transportation Sector Low-Carbon (TSLC) Research Developing After the Paris Agreement (PA)? A Decade Review," Sustainability, MDPI, vol. 17(5), pages 1-28, March.
    16. Hongxin Yu & Yaohui Jiang & Zhaowen Zhang & Wen-Long Shang & Chunjia Han & Yuanjun Zhao, 2022. "The impact of carbon emission trading policy on firms’ green innovation in China," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-24, December.
    17. Bao, Zhaoyao & Li, Jiapei & Bai, Xuehan & Xie, Chi & Chen, Zhibin & Xu, Min & Shang, Wen-Long & Li, Hailong, 2023. "An optimal charging scheduling model and algorithm for electric buses," Applied Energy, Elsevier, vol. 332(C).
    18. Wu, Chenguang & Zhang, Jie & Yang, Cheng & Lu, Dagang, 2025. "Probabilistic assessment of a road network subjected to rainfall-induced landslides," Reliability Engineering and System Safety, Elsevier, vol. 264(PB).
    19. Lv, Zhihan & Wang, Nana & Lou, Ranran & Tian, Yajun & Guizani, Mohsen, 2023. "Towards carbon Neutrality: Prediction of wave energy based on improved GRU in Maritime transportation," Applied Energy, Elsevier, vol. 331(C).
    20. Wei Deng & Junqi Deng & Peyman Arebi, 2025. "Detection of Effective Devices in Information Dissemination on the Complex Social Internet of Things Networks Based on Device Centrality Measures," Complexity, John Wiley & Sons, vol. 2025(1).
    21. Singh, Aradhana & Khetarpal, Ritish & Rai, Amod, 2025. "Role of spatial embedding and planarity in shaping the topology of the Street Networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 677(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:complx:v:2022:y:2022:i:1:n:5451017. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://onlinelibrary.wiley.com/journal/8503 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.