Railway capacity calculation in emergency using modified fuzzy random optimization methodology

Wang, L, An, M ORCID: https://orcid.org/0000-0002-1069-7492, Jia, L and Qin, Y 2020, Railway capacity calculation in emergency using modified fuzzy random optimization methodology , in: The 4th International Conference on Electrical and Information Technologies for Rail Transportation (EITRT) 2019, 25-27 October 2019, Changsha, China.

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Abstract

Accurate estimated capacity of the railway section can provide reliable information to railway operators and engineers in decision-making, particularly, in an emergency situation. However, in an emergency, the optimization of capacity of a railway section is usually involved to study, for example, the characteristics of dynamic, fuzziness, randomness, and non-aftereffect properties. This paper presents a proposed capacity calculation method based on the modified fuzzy Markov chain (MFMC). In this method, the capacity of a railway section in an emergency can be expressed by a fuzzy random variable, which remains the randomness of capacity changing according to the impact of emergencies and the fuzziness of the driving behavior and other factors.Acase study of a high-speed line from Beijing to Shanghai is used to show the process of the proposed methods for optimization of section capacity calculation in an emergency.

Item Type: Conference or Workshop Item (Paper)
Schools: Schools > School of the Built Environment > Centre for Urban Processes, Resilient Infrastructures & Sustainable Environments
Journal or Publication Title: Proceedings of the 4th International Conference on Electrical and Information Technologies for Rail Transportation (EITRT) 2019: Rail Transportation Information Processing and Operational Management Technologies
Publisher: Springer
Series Name: Lecture Notes in Electrical Engineering
ISBN: 9789811529139 (print); 9789811529146 (online)
ISSN: 1876-1100
Related URLs:
Funders: NationalKey Research andDevelopment Program of China, National Natural Science Foundation of China
Depositing User: Professor Min An
Date Deposited: 28 May 2020 07:57
Last Modified: 28 May 2020 12:10
URI: http://usir.salford.ac.uk/id/eprint/56877

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