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; Zhang, Y; Rana, K

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.

Citation

Wang, L., An, M., Zhang, Y., & Rana, K. (2017, October). Railway network reliability analysis based on key station identification using complex network theory : a real-world case study of high-speed rail network. Presented at International Research Conference 2017 : Shaping Tomorrow's Built Environment, University of Salford, UK

• Presentation Conference Type: Other
• Conference Name: International Research Conference 2017 : Shaping Tomorrow's Built Environment
• Conference Location: University of Salford, UK
• Start Date: Oct 11, 2017
• End Date: Oct 12, 2017
• Acceptance Date: Mar 18, 2023
• Publication Date: Oct 12, 2017
• Deposit Date: Nov 12, 2018
• Publicly Available Date: Nov 12, 2018
• Publisher URL: https://usir.salford.ac.uk/44058/
• Additional Information: Event Type : Conference; Funders : National Natural Science Foundation of China;Beijing Jiaotong University State Key Laboratory of Rail Traffic and Control and Safety; Projects : National Key Research and Development Programme;Railway Infrastructure Safety Risk Quantitative Analysis Technology; Grant Number: 2016YFB1200401; Grant Number: RCS2017K001 RCS2016ZT016