Volume 21 Issue 5
Nov.  2021
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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2021  >  21(5): 227-237
YE Mao, QIAN Zhong-wen, LI Jun-cheng, CAO Cong-yong. Selection and optimization model of standby train deployment stations on urban rail transit for large passenger flow[J]. Journal of Traffic and Transportation Engineering, 2021, 21(5): 227-237. doi: 10.19818/j.cnki.1671-1637.2021.05.019
Citation: YE Mao, QIAN Zhong-wen, LI Jun-cheng, CAO Cong-yong. Selection and optimization model of standby train deployment stations on urban rail transit for large passenger flow[J]. Journal of Traffic and Transportation Engineering, 2021, 21(5): 227-237. doi: 10.19818/j.cnki.1671-1637.2021.05.019
shu

Selection and optimization model of standby train deployment stations on urban rail transit for large passenger flow

doi: 10.19818/j.cnki.1671-1637.2021.05.019
  • 1.

    School of Automation, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu, China

  • 2.

    MIIT Key Lab of Traffic Information Fusion and System Control, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu, China

  • 3.

    Guangzhou Metro Group Co., Ltd., Guangzhou 510380, Guangdong, China

Funds:

National Key Research and Development Program of China 2017YFB1201202

Transportation Science and Technology Project of Jiangsu Province 2020Y17

More Information
  • Author Bio:

    YE Mao(1982-), male, associate professor, PhD, yemao0924@163.com

  • Received Date: 2021-06-18
    Available Online: 2021-11-13
  • Publish Date: 2021-10-01
    Abstract
  • In order to quickly relieve the large passenger flow of stations on urban rail transit lines and reduce the total waiting time of passengers, the problem of standby train deployment was studied. Based on the consideration of train tracking relationship, train dwelling time, and other constraints, a multi-objective optimization model was established to determine the timing of standby train deployment, select the best station, and dynamically adjust the schedule. The conditions for the deployment of standby trains were defined and a quantitative determination method for the timing of the standby train operation was proposed. A 0-1 variable was used to characterize whether the station was equipped for standby trains, and it was used as the model input. Then, a mixed integer nonlinear programming (MINP) model of standby train deployment was established to minimize the waiting time of passengers at the station with large passenger flow, and the deviation time (delay time) of the timetable was constructed. The model compared the efficiency of different standby train deployment schemes to get the best standby train deployment station and the subsequent operation plan. An improved particle swarm optimization algorithm with a penalty function was designed to deal with the 0-1 variable and continuous variables simultaneously. Research results show that the method can make plans for all stations satisfying the conditions of standby train deployment, and further select the best standby train stations from the alternative stations. The maximum total passenger waiting time reduces by 1 318 209 s, and the optimization efficiency reduces about 21.9%. Moreover, the improved particle swarm optimization algorithm has good applicability to the MINP model. Compared to the existing urban rail line train operation adjustment and schedule optimization methods, the proposed method provides a more quantitative judgment on the timing of the standby train deployment in response to large passenger flow situations. It provides the evacuation capacity and efficiency of stations with large passenger flow stations and optimizes the operation plans of the standby and subsequent trains. The problem of large passenger flows at stations during peak hours can be relieve effectively. 4 tabs, 9 figs, 30 refs.

     

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