Vulnerability Analysis of Urban Rail Transit Network within Multi-Modal Public Transport Networks

Lu, Qing-Chang; Lin, Shu

doi:10.3390/su11072109

Cited by 26 publications

(4 citation statements)

References 28 publications

(55 reference statements)

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“…Jiang et al [12] proposed an emergency material vehicle dispatching and routing model which has multi-objective and multi-dynamic-constraint. Lu et al [13] presented an approach to measure and analyze the vulnerability of urban rail transit network based on accessibility, which explicitly considers the characteristics of rail transit passenger flow, travel cost changes and alternative modes of transportation. Deng et al [14] designed for passenger bus bridge circuit in the case of urban rail traffic interruption.…”

Section: Literature Reviewmentioning

confidence: 99%

A Time-Space Network Model Based on a Train Diagram for Predicting and Controlling the Traffic Congestion in a Station Caused by an Emergency

2019

Symmetry

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Timely predicting and controlling the traffic congestion in a station caused by an emergency is an important task in railway emergency management. However, traffic forecasting in an emergency is subject to a dynamic service network, with uncertainty surrounding elements such as the capacity of the transport network, schedules, and plans. Accurate traffic forecasting is difficult. This paper proposes a practical time-space network model based on a train diagram for predicting and controlling the traffic congestion in a station caused by an emergency. Based on the train diagram, we constructed a symmetric time-space network for the first time by considering the transition of the railcar state. On this basis, an improved A* algorithm based on the railcar flow route was proposed to generate feasible path sets and a dynamic railcar flow distribution model was built to simulate the railcar flow distribution process in an emergency. In our numerical studies, these output results of our proposed model can be used to control traffic congestion.

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Section: Literature Reviewmentioning

confidence: 99%

A Time-Space Network Model Based on a Train Diagram for Predicting and Controlling the Traffic Congestion in a Station Caused by an Emergency

2019

Symmetry

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“…Such disruptive events may affect public transport infrastructure or service at different spatial scales, generally causing the failure or partial unavailability of one or more elements of the transport network. The consequent decline in service level is usually higher than the typical variability experienced during day-to-day travels [7,8].…”

Section: Introductionmentioning

confidence: 97%

Public Transport Network Vulnerability and Delay Distribution among Travelers

et al. 2021

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Methodologies and approaches for assessing the vulnerability of a public transport network are generally based on quantifying the average delay generated for passengers by some type of disruption. In this work, a novel methodology is proposed, which combines the traditional approach, based on the quantitative evaluation of averaged disruption effects, with the analysis of the asymmetry of effects among users, by means of Lorenz curves and Gini index. This allows evaluating whether the negative consequences of disruptions are equally spread among passengers or if differences exist. The results obtained show the potential of the proposed method to provide better knowledge about the effects of a disruption on a public transport network. Particularly, it emerged that disrupted scenarios that appear similar in terms of average impacts are actually very different in terms of the asymmetry of effects among users.

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“…Credited for the advantages of high speed, safety, environmental protection, the urban rail transit has become the first option of transportation [1], [2]. However, some incidents (such as severe weather, sudden disaster, and terrorist attack) may lead to the subway station disruption [3]- [5]. Therefore, it is essential for us to investigate the robustness of subway system, which can largely ensure the reliance and efficiency of service that the subway system provides.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Robustness Analysis for Subway Network With Spatiotemporal Characteristic of Passenger Flow

et al. 2020

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The robustness is a crucial and essential problem of a subway network (SN), which can help us improve the efficiency of a transportation system. Several existing researches have analyzed the SN robustness based on the rail structure or the static distribution of passenger flow. However, the spatiotemporal characteristic of passenger flow also plays an important role in the SN robustness, since it can trigger some unexpected cascading failures in SN. Therefore, how to characterize the effect of this cascading failure on the SN robustness still remains an important and open problem. In this paper, we address the above problem as follows: (1) we propose a temporal subway network (TSN) to consider the dynamics of passenger flow in SN; (2) we adopt the linear threshold (LT) model to simulate the cascading failure process of TSN and propose a new robustness metric R(t) to evaluate the effect of this cascading failure on SN robustness. Based on the Shanghai subway smart card data, we carry out extensive experiments to analyze the effects of the cascading failure on the Shanghai SN robustness. Experiments show that the Shanghai TSN robustness varies over time. More significantly, the large volume of passenger flow can increase the impact of failure modes (i.e., random and malicious failure modes) on the Shanghai TSN robustness. INDEX TERMS Subway network, robustness, dynamic passenger flow, cascading failure.

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Vulnerability Analysis of Urban Rail Transit Network within Multi-Modal Public Transport Networks

Cited by 26 publications

References 28 publications

A Time-Space Network Model Based on a Train Diagram for Predicting and Controlling the Traffic Congestion in a Station Caused by an Emergency

A Time-Space Network Model Based on a Train Diagram for Predicting and Controlling the Traffic Congestion in a Station Caused by an Emergency

Public Transport Network Vulnerability and Delay Distribution among Travelers

Dynamic Robustness Analysis for Subway Network With Spatiotemporal Characteristic of Passenger Flow

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