Cascading Failures on Multimodal Public Transportation Networks: The Role of Station Coupling Strength

公共交通 计算机科学 运输工程 工程类 计算机网络
作者
Jing Li,Qing-Chang Lu,Peng-Cheng Xu,Shixin Wang,Chi Xie
出处
期刊:IEEE Transactions on Intelligent Transportation Systems [Institute of Electrical and Electronics Engineers]
卷期号:25 (11): 17187-17199 被引量:10
标识
DOI:10.1109/tits.2024.3450019
摘要

While many studies explore cascading failures on single transit network, most of them fail to interpret the spread of failures between multimodal public transportation systems. Relevant research tends to measure station coupling strength under normal conditions, but this strength would change due to the variations of network topology, transportation, and travel characteristics under cascading failures. To address the above issues, this study proposes a multimodal coupled map lattice model addressing station coupling strength during failures. This model aims to explore the spread of failures across coupled multimodal transit networks, identifying the paths and intensity of cascading failures between different public transport modes. It also examines the impacts of station coupling asymmetry, resulting from transportation efficiency and operation modes of different transit systems on cascading failures. The proposed model is then applied to the metro-bus coupled networks of Shenzhen, China. The results indicate that cascading failures on metro network would be alleviated when coupled with bus network. However, cascading failures are magnified on bus network when coupled with metro network. On the metro-bus coupled networks, failures of stations/stops with higher station coupling strength would cause more serious cascading failures than those of failures of important stations or stops on single network. In addition, the spread speed of cascading failures on metro-bus coupled networks depends largely on the number of failed metro stations. Findings of this work would offer valuable insights for the planning of robust metro-bus coupled systems and efficient emergency responses to avoid large-scale network failures.
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