Fletcher, D. orcid.org/0000-0002-1562-4655, Harrison, R. orcid.org/0000-0002-9323-8637, Karmakharm, T. et al. (2 more authors) (2018) RateSetter: roadmap for faster, safer, and better platform train interface design and operation using evolutionary optimisation. In: Aguirre, H.E. and Takadama, K., (eds.) GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO '18: Genetic and Evolutionary Computation Conference, 15-19 Jul 2018, Kyoto, Japan. Association for Computing Machinery , pp. 1230-1237. ISBN 9781450356183
Abstract
There is a challenge ahead in the rail industry to accommodate increased demand. Time spent at the platform train interface (PTI) as passengers board and alight, rather than on the move, represents a limitation on system capacity. To overcome this, we propose RateSetter: an evolutionary optimiser that for the first time provides more effective PTI design choices based on passenger flow time and safety. An agent based passenger simulation model validated with CCTV footage is employed for fitness evaluation. The initial results provide guidelines not only for future PTI designs but also for retrofit designs to existing infrastructure, evaluating the effectiveness and diminishing returns of PTI features for the considered scenarios. Furthermore, it is observed that the proposed optimal PTI designs could significantly reduce the flow time for the cases examined. Results show that retro-fit designs could reduce the flow time in the range of 10%-35%.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 Association for Computing Machinery. This is an author-produced version of a paper subsequently published in GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | real-world applications; optimisation; platform train interface |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Mechanical Engineering (Sheffield) The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 04 May 2023 13:10 |
Last Modified: | 04 May 2023 15:43 |
Status: | Published |
Publisher: | Association for Computing Machinery |
Refereed: | Yes |
Identification Number: | 10.1145/3205455.3205605 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:198423 |