Passenger flow control is the most direct and effective way to solve the problem of metro line congestion. To solve the problem that the random arrival characteristics of passenger flow affect the reliability of the passenger flow control strategy, this paper provides a robust passenger flow control strategy for a metro line under a short-turning operation pattern in peak hours according to the dynamic, time-varying and random characteristics of passenger flow. Firstly, a short-turning operation pattern environment is constructed, and the arrival rate of historical inbound passenger flow is obtained by statistical analysis. Poisson process is used as a parameter to generate multi-scene random arrival passenger flow data. Secondly, a robust passenger flow control and ride strategy is proposed considering the passenger flow OD of the stations inside and outside the short-turning sections. Combining with the strategy, a robust passenger flow collaborative control model is constructed with multi-scene random arrival passenger flow data as input. Finally, an empirical study was carried out on the Beijing Fangshan metro line. The results show that the robust passenger flow control strategy is applicable to the three passenger flow scenarios, and can ensure the passengers’ safety in the three passenger flow scenarios. Moreover, compared with the separate passenger flow control in a single passenger flow scenario, the passenger waiting time under the robust passenger flow control strategy only increases by 1.19%, 4.47% and 4.42% respectively, which basically ensures the travel efficiency of passengers.


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    Title :

    Research on Robust Passenger Flow Control Model of Metro Under a Short-Turning Operation Pattern


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Jia, Limin (editor) / Zhang, Qiang (editor) / Xie, Zhengyu (editor) / Li, Haibin (editor) / Yong, Kenan (editor) / Wang, Li (editor) / Xue, Hongjiao (author) / Jia, Limin (author) / Peng, Fei (author) / Sun, Xuan (author)

    Conference:

    International Conference on Artificial Intelligence and Autonomous Transportation ; 2024 ; Beijing, China December 06, 2024 - December 08, 2024



    Publication date :

    2025-04-19


    Size :

    14 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English