Yin, H, Wu, J, Sun, H et al. (2 more authors) (2019) Optimizing last trains timetable in the urban rail network: social welfare and synchronization. Transportmetrica B: Transport Dynamics, 7 (1). pp. 473-497. ISSN 2168-0566
Abstract
Last train timetable design is to coordinate last train services from different lines in an urban rail network for maximizing the number of transfers. It is a challenging operational research problem to balance the competing demand of two decision agents: that of the government agencies to provide the best social services with minimal government subsidy, and that of the train operating companies to minimize operating costs. A bi-level programming model is formulated for the last train timetabling problem, in which the upper level is to maximize the social service efficiency, and the lower level is to minimize the revenue loss for the operating companies. To solve this problem, a genetic algorithm combined with an active-set approach is developed. We report the optimization results on real-world cases of the Beijing subway network. The results show that the optimized last train timetable can significantly improve the transfer coordination.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This is an Accepted Manuscript of an article published by Taylor & Francis in Transportmetrica B: on 5th March 2018, available online: https://doi.org/10.1080/21680566.2018.1440361. |
Keywords: | Last train; Timetable; Urban railway network; Bi--level programming; government subsidy |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) > ITS: Spatial Modelling and Dynamics (Leeds) |
Funding Information: | Funder Grant number RSSB Rail Safety & Standards Board T1071-02 Royal Academy of Engineering No External Reference |
Depositing User: | Symplectic Publications |
Date Deposited: | 08 Mar 2018 13:43 |
Last Modified: | 11 Apr 2019 12:37 |
Status: | Published |
Publisher: | Taylor & Francis |
Identification Number: | 10.1080/21680566.2018.1440361 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:128338 |