IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i24p7230-d299712.html
   My bibliography  Save this article

Urban Rail Transit Passenger Flow Forecasting Method Based on the Coupling of Artificial Fish Swarm and Improved Particle Swarm Optimization Algorithms

Author

Listed:
  • Yuan Yuan

    (Key Laboratory of Transport Industry Big Data Application Technologies for Comprehensive Key Laboratory of Transport, Beijing Jiaotong University, Beijing 100044, China
    School of Automotive and Transportation, Shenzhen Polytechnic College, Shenzhen 518055, China)

  • Chunfu Shao

    (Key Laboratory of Transport Industry Big Data Application Technologies for Comprehensive Key Laboratory of Transport, Beijing Jiaotong University, Beijing 100044, China)

  • Zhichao Cao

    (School of Transportation and Civil Engineering, Nantong University, Nantong 226019, China)

  • Wenxin Chen

    (Key Laboratory of Transport Industry Big Data Application Technologies for Comprehensive Key Laboratory of Transport, Beijing Jiaotong University, Beijing 100044, China)

  • Anteng Yin

    (School of Architecture, Harbin Institute of Technology, Harbin 150001, China
    Kunming Urban Planning & Design Institute, Kunming 650041, China)

  • Hao Yue

    (Key Laboratory of Transport Industry Big Data Application Technologies for Comprehensive Key Laboratory of Transport, Beijing Jiaotong University, Beijing 100044, China)

  • Binglei Xie

    (School of Architecture, Harbin Institute of Technology, Harbin 150001, China)

Abstract

Urban rail transit passenger flow forecasting is an important basis for station design, passenger flow organization, and train operation plan optimization. In this work, we combined the artificial fish swarm and improved particle swarm optimization (AFSA-PSO) algorithms. Taking the Window of the World station of the Shenzhen Metro Line 1 as an example, subway passenger flow prediction research was carried out. The AFSA-PSO algorithm successfully preserved the fast convergence and strong traceability of the original algorithm through particle self-adjustment and dynamic weights, and it effectively overcame its shortcomings, such as the tendency to fall into local optimum and lower convergence speed. In addition to accurately predicting normal passenger flow, the algorithm can also effectively identify and predict the large-scale tourist attractions passenger flow as it has strong applicability and robustness. Compared with single PSO or AFSA algorithms, the new algorithm has better prediction effects, such as faster convergence, lower average absolute percentage error, and a higher correlation coefficient with real values.

Suggested Citation

  • Yuan Yuan & Chunfu Shao & Zhichao Cao & Wenxin Chen & Anteng Yin & Hao Yue & Binglei Xie, 2019. "Urban Rail Transit Passenger Flow Forecasting Method Based on the Coupling of Artificial Fish Swarm and Improved Particle Swarm Optimization Algorithms," Sustainability, MDPI, vol. 11(24), pages 1-13, December.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:24:p:7230-:d:299712
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/24/7230/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/11/24/7230/
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ying Lu & Shuqi Sun, 2020. "Scenario-Based Allocation of Emergency Resources in Metro Emergencies: A Model Development and a Case Study of Nanjing Metro," Sustainability, MDPI, vol. 12(16), pages 1-21, August.
    2. Ting Chen & Jianxiao Ma & Shuang Li & Zhenjun Zhu & Xiucheng Guo, 2023. "Dynamic Evaluation Method for Mutation Degree of Passenger Flow in Urban Rail Transit," Sustainability, MDPI, vol. 15(22), pages 1-17, November.

    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:gam:jsusta:v:11:y:2019:i:24:p:7230-:d:299712. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.