Modelling cascade dynamics of passenger flow congestion in urban rail transit network induced by train delay

Li, M, Zhou, X, Wang, Y, Jia, L and An, M ORCID: https://orcid.org/0000-0002-1069-7492 2022, 'Modelling cascade dynamics of passenger flow congestion in urban rail transit network induced by train delay' , Alexandria Engineering Journal, 61 (11) , pp. 8797-8807.

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Abstract

A research into congestion propagation mechanism of urban rail transit passenger flow in train delay scenario is of great significance to the formulation of coordinated limitation measures of passenger flow among the operation lines in urban rail transit networks. This paper presents a methodology for modelling cascade dynamics of passenger flow congestion. In this method, firstly, a computing method of network passenger flow states based on load entropy is proposed to describe the overall load conditions of passenger flow in the network. Secondly, a rail transit network is developed and the computation formulas of load and capacity of nodes have been established combining with a comprehensive consideration of nodes’ topological passenger flow attributes. Thirdly, Trip Betweenness Centrality of nodes is proposed and calculated. Considering the function of rail transit line capacity and load distribution strategy, the passenger flow congestion in train delay scenario is established to calculate cascade dynamics of congestion and the state transition of urban rail transit network. A real case study on Beijing Metro Line 10 is used to demonstrate the proposed methodology. The results show that the load values of nodes and the distances from the initial failure nodes can be determined for the spreading scope of passenger flow congestion in disrupted metro line and adjacent metro lines.

Item Type: Article
Schools: Schools > School of the Built Environment > Centre for Urban Processes, Resilient Infrastructures & Sustainable Environments
Journal or Publication Title: Alexandria Engineering Journal
Publisher: Elsevier
ISSN: 1110-0168
Related URLs:
Funders: State Key Laboratory of Rail Traffic Control and Safety, Youth Program of the National Natural Science Foundation
Depositing User: Professor Min An
Date Deposited: 08 Mar 2022 10:42
Last Modified: 08 Mar 2022 10:45
URI: http://usir.salford.ac.uk/id/eprint/63335

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