Evacuation Demand Prediction under Metro Rail Disruptions Based on Normal Historical Data

Historical data of metro rail passenger volumes under disruptions, such as line and station closure, is hard to collect, leading to the difficulty in predicting evacuation demand under metro disruptions and emergency response strategy design. It becomes the bottleneck of the emergency response as well. To fill this gap, the authors develop a method to predict evacuation demand under metro disruptions mainly using historical data obtained from the natural state, when no shocks take place. The authors first formulate the mathematical representation of the evacuation demand of every type of metro station. Input variables in this step are features related to the station under normal state. Then based on these mathematical expressions, the authors develop a simulation system to imitate the spatio-temporal evolution of passenger demand within the whole network under disruptions. The metro capacity drop under disruptions is also used to describe the disruption situation. Several typical scenarios from the Shanghai metro network are used as examples to validate the proposed method. The results show that, the authors' method could give the prediction of evacuation demand and its evolvement, as well as model how severe stations will be affected by given disruptions. The authors' simulation results show that, the most vulnerable stations under disruption, which are the locations where peak stranded passenger volume takes place, are mainly turn-back stations, closed stations, and the transfer stations near closed stations. This paper provides new insight into evacuation demand prediction under disruptions. It could be used by transport authorities to better respond to the metro system disruption.


  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: 16p

Subject/Index Terms

Filing Info

  • Accession Number: 01764423
  • Record Type: Publication
  • Report/Paper Numbers: TRBAM-21-04263
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 23 2020 11:05AM