He, L, Chen, L, Liu, J orcid.org/0000-0002-3808-5957 et al. (3 more authors) (2022) Passenger Flow-Oriented Metro Operation without Timetables. Applied Sciences, 12 (10). 4999. p. 4999. ISSN 2076-3417
Abstract
Unpredictable fluctuant passenger flow usually exists in urban metro operations. In this situation, traditional predetermined metro timetables cannot always meet the variation of passenger flow, and thus the service quality of the metro system could be affected profoundly. In this paper, by introducing an innovative metro operation method without timetables, we develop a nonlinear integer programming model to continually optimise the train operation to deal with detected real-time passenger flow variations. We aim to minimise the total passenger waiting time in the research time horizon under the vehicle number constraint. A modified genetic algorithm integrated with a macroscopic metro simulator is adopted to solve the proposed model. A case study based on the Beijing Metro Line 19 is implemented to provide a quantitative result for evaluating the proposed passenger flow-oriented metro operation method without timetables. Compared to traditional timetable-based metro operation, the method could significantly improve the metro operation’s flexibility and the quality of services.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
Keywords: | metro operation; genetic algorithm; passenger flow; optimisation |
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) |
Depositing User: | Symplectic Publications |
Date Deposited: | 24 May 2022 10:02 |
Last Modified: | 24 May 2022 10:02 |
Status: | Published |
Publisher: | MDPI |
Identification Number: | 10.3390/app12104999 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:187208 |