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Route redundancy-based approach to identify the critical stations in metro networks: A mean-excess probability measure

机译：路由基于冗余的方法来识别地铁网络中的关键站：均衡概率测量

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摘要

Identifying the critical elements of metro networks attracts a growing attention due to the significant impact of accidents on metro systems. The existing measures can be divided into two types: the localized measures (e.g., flow, centrality, etc.) in a normal network state and the impact-based measures by assuming failure scenarios to conduct before-and-after analysis. In this paper, we develop a new method to identify the critical stations in metro systems based on the concept of route redundancy. Different from the localized measures, route redundancy describes the origin-destination effective connections under potential disruptions explicitly from travelers' perspective. Compared with the impact-based measures, the proposed method does not need to enumerate disruption scenarios and to reevaluate the resultant network performances. Specifically, the mean-excess criticality probability is proposed as a risk measure to calculate the criticality of each station in a metro network. A realistic case study based on the Shanghai metro network is conducted to demonstrate the features of the proposed method. The results indicate that the critical stations are not necessarily transfer stations or those with a high degree, and the important stations based on betweenness, passenger flow and network efficiency are not necessarily critical for the network redundancy. The proposed method could assist in a cost-effective resource allocation and an informed decision-making for strategically enhancing the metro network resiliency.
机译：识别地铁网络的临界要素由于事故发生在地铁系统的重大影响，引起了不断的关注。现有措施可分为两种类型：通过假设在分析前后进行的失败方案进行普通网络状态和基于冲击的措施的本地化措施（例如，流动，中心等）。在本文中，我们开发了一种新方法，以确定基于路径冗余概念的地铁系统中的关键站。与本地化措施不同，路由冗余介绍了从旅行者的角度明确的潜在中断的原始目的地有效连接。与基于冲击的措施相比，所提出的方法不需要枚举中断场景并重新评估所产生的网络性能。具体地，平均过度的临界概率被提出为计算地铁网络中每个站的临界性的风险衡量标准。基于上海地铁网络的一个现实案例研究，展示了所提出的方法的特征。结果表明，关键站不一定是转移站或具有高度的那些，以及基于之间的重要站，乘客流量和网络效率对于网络冗余不一定至关重要。该方法可以帮助有效的资源分配和明智的决策，以便战略性地增强地铁网络弹性。

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