Railway network reliability analysis based on key station identification using complex network theory : a real-world case study of high-speed rail network

Wang, Li, An, M ORCID: https://orcid.org/0000-0002-1069-7492, Zhang, Y and Rana, K 2017, Railway network reliability analysis based on key station identification using complex network theory : a real-world case study of high-speed rail network , in: International Research Conference 2017 : Shaping Tomorrow's Built Environment, 11-12 October 2017, University of Salford, UK.

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Abstract

The railway infrastructures have been rapidly developed around the world in the recent years. As a consequence, topology structures and operation modes of the railway network are greatly changed to very complicated network systems. Reliability analysis of a railway network combining topology structures with operation functions will help to optimize the railway network infrastructures. This paper presents a new reliability analysis method of the railway network, combining the physical topology with operation strategies. Firstly, two network models of railway physical network and train flow network are proposed. Then key stations identification indexes can be gained from such two network models, which include degree, strength, betweenness clustering coefficient and a comprehensive index. Given the key stations, railway network efficiency can be analysed under selective and random modes of the stations failure. A real-world case study of the high-speed railway network in China is presented to demonstrate the key stations playing an important role in improving the whole network reliability. In the end, some recommendations are given to improve the network reliability. The proposed method can provide useful information to railway developers, designers and engineers in the railway infrastructure projects for sustainable development.

Item Type: Conference or Workshop Item (Paper)
Schools: Schools > School of the Built Environment
Journal or Publication Title: International Research Conference 2017 : Shaping Tomorrow's Built Environment
Publisher: University of Salford
Funders: National Natural Science Foundation of China, Beijing Jiaotong University State Key Laboratory of Rail Traffic and Control and Safety
Depositing User: Professor Min An
Date Deposited: 12 Nov 2018 10:48
Last Modified: 29 Mar 2019 10:15
URI: http://usir.salford.ac.uk/id/eprint/48920

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