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Integrated passenger terminal capacity analysis under the capacity constraints of surrounding road network

Author

Listed:
  • Zhou, Xuemei
  • Huang, Huang
  • Jia, Xinchang
  • Jiang, Nan

Abstract

Forecasting the demand of an integrated passenger terminal under the capacity constraints of its surrounding roads has always been a challenging problem for urban transportation terminals planning and construction in China. A bi-level model is proposed to forecast the passenger demand of an integrated terminal, whose throughput is restricted by the capacity of its surrounding road network. The upper level model maximizes the passenger flow of a terminal considering the capacity constraints of its surrounding road network. The User Equilibrium (UE) model is adopted as lower level model for traffic assignment. The adaptive genetic algorithm (AGA) is applied to solve this bi-level problem. The West-Lanzhou integrated passenger terminal in the Lanzhou city of China is used to test the feasibility and effectiveness of the proposed model. Test results show that, to reduce traffic congestion at the terminal, priority should be given to transportation mode with higher performance.

Suggested Citation

  • Zhou, Xuemei & Huang, Huang & Jia, Xinchang & Jiang, Nan, 2014. "Integrated passenger terminal capacity analysis under the capacity constraints of surrounding road network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 414(C), pages 194-203.
    • Handle: RePEc:eee:phsmap:v:414:y:2014:i:c:p:194-203
    DOI: 10.1016/j.physa.2014.07.003
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    References listed on IDEAS

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    Cited by:

    1. Tang, Jinjun & Yang, Yifan & Qi, Yong, 2018. "A hybrid algorithm for Urban transit schedule optimization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 745-755.
    2. Wu, Shanhua & Yang, Zhongzhen, 2018. "Optimizing location of manufacturing industries in the context of economic globalization: A bi-level model based approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 327-337.

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