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Infrastructure Resilience Conference 2018

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An Integrated Framework for Detecting the Critical Nodes of an Urban Transit Network

Urban transit networks including bus and rail modes are prone to disruption such as route/station ‎breakdowns and noticeable delays. Breakdowns may be due to terrorist attacks or simply because ‎of technical issues. According to high number of passengers using transit services, disruptions ‎may cause huge consequences and also influence the reliability and utility of public transit.‎ In this paper, we have proposed an integrated framework for detecting the most critical stations ‎of an urban bus network. Applying this framework to urban transit systems could give hints to ‎urban managers on the locations that need the most surveillance. This surveillance is not only in ‎terms of security provision but also traffic related as congestion may be a main cause of transit ‎service disruption.‎ The proposed framework consists of three main steps. In the first step, the network is evaluated ‎to find whether it is scale-free or not. If so, the nodes with highest degree are registered in the ‎list of critical nodes.‎‏ ‏‎ In the second step, the risk of delay is calculated for each bus line based on ‎the product of delay probability and its consequence. The delay probability is calculated based on ‎the length of the line passing through congested streets and the level of congestion in those ‎streets. The intensity of consequence is calculated based on the number of passengers of the line ‎and those transferring to and from it. In the third step of the framework, the collapse of the giant ‎component of the bus network as the result of node removals is analyzed.‎ The proposed framework is applied to the bus network of Isfahan (Iran) consisting 64 lines and ‎approximately 1700 stations. The probability distribution of node degrees followed power law ‎and it revealed that the network is scale-free. Therefore, the network is vulnerable in regards of ‎disruptions in its few high-degree stations but is rather robust against random failures. The critical ‎stations of the network are determined. Moreover, the line segments and stations with highest ‎risk of delay are determined. As the third step of the framework, the variation of the size of giant ‎component of the network caused by the removal of its nodes was investigated. Results indicate ‎that among betweenness centrality, closeness centrality and number of passengers of each line, ‎the network is most vulnerable to losing nodes with higher betweenness centrality.‎ The proposed framework could be used to list the most critical transit stations from the integrity, ‎reliability and robustness points of view.‎

Meisam Akbarzadeh
Isfahan University of Technology
Iran

Roxana Rasouli
Isfahan University of Technology
Iran

 

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