Passenger evacuation at a malfunctioning urban rail station based on interdependent networks

Qingyu Luo (), Jinge Song (), Tianyao Zheng () and Lili Yang
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Qingyu Luo: School of Transportation, Jilin University, Changchun 130022, P. R. China
Jinge Song: School of Transportation, Jilin University, Changchun 130022, P. R. China
Tianyao Zheng: Shenzhen Transportation Design and Research Institute Co., Ltd, No. 9, Tianbei 4th Road, Luohu District, Shenzhen Shenzhen, Guangdong 518020, P. R. China
Lili Yang: School of Transportation, Jilin University, Changchun 130022, P. R. China

International Journal of Modern Physics C (IJMPC), 2019, vol. 30, issue 11, 1-23

Abstract: In the context of urban rail evacuation analysis, little work addresses the mechanism of passenger propagation between urban rail and bus systems. This paper attempts to quantify the propagation mechanism to make evacuation scheduling reasonable and effective. First, it provides a set of methods for characterizing the law of passenger propagation in the region affected by a rail station failure, based on the theory of interdependent networks. An interdependent public transport network between the rail transit and buses in the affected region is constructed using the methods of Space L and Space P. In the network, the initial capacities are modeled, the redistribution rules of passengers and the status identification rules of stations are established, and the propagation results are characterized. Second, using the passenger propagation impact of a faulty rail station, an emergency bus-scheduling model is set up, aimed at minimizing the operating costs of emergency buses and maximizing the benefits for passengers. A genetic algorithm is devised to solve the scheduling model. Finally, an example is put forward to verify the feasibility of the designed methods. The example shows that the methods provide theoretical support and a decision aid for passenger evacuation strategies at faulty urban rail stations.

Keywords: Rail transit; emergency bus; complex network; scheduling model; cascading failure (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1142/S0129183119500980

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