A Multi-modal Evacuation Model for Metro Disruptions: Based on Automatic Fare Collection Data in Shanghai, China

Quick and efficient recovery plan for metro disruption is vital to the alleviation of congestion in the affected areas. This paper proposes a multi-modal evacuation model to help metro transit organizations plan for and manage unplanned service disruptions. The model enjoys three main features. (1)Weighted average optimization method is introduced to better measure passenger utilities in disruption. Four factors are including: waiting time, walking time, in-vehicle time and transfer times, which is based on a previous work by the author. (2) The model offers a multi-modal recovery plan not only in consideration of bus bridging. (3) Besides, the model is designed in a framework partially fixed to better adapt the unpredictable and randomness feature of disruption, which means responding instantly to real-time feedback information, thus could include the updating evacuation circumstances as known conditions. The model outputs the evacuation plan including estimated evacuee population, transfer points and bus bridging strategies. Finally, the feasibility of the model has been demonstrated with the test on automatic fare collection data from a real metro disruption case in Shanghai, China.

  • Supplemental Notes:
    • This paper was sponsored by TRB committee AP065 Standing Committee on Rail Transit Systems.
  • Corporate Authors:

    Transportation Research Board

    500 Fifth Street, NW
    Washington, DC  United States  20001
  • Authors:
    • Dai, Xiaoqing
    • Tu, Huizhao
    • Sun, LiJun
  • Conference:
  • Date: 2016

Language

  • English

Media Info

  • Media Type: Web
  • Features: Figures; Maps; References; Tables;
  • Pagination: 15p
  • Monograph Title: TRB 95th Annual Meeting Compendium of Papers

Subject/Index Terms

Filing Info

  • Accession Number: 01595797
  • Record Type: Publication
  • Report/Paper Numbers: 16-5660
  • Files: PRP, TRIS, TRB, ATRI
  • Created Date: Apr 11 2016 8:38AM