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

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  • 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
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    Citations

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    Cited by:

    1. 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.
    2. 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.

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