Bus Network Design and Frequency Setting in the Post-COVID-19 Pandemic: The Case of London
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 149, Issue 4
Abstract
A transit network design frequency setting model is proposed to cope with the postpandemic passenger demand. The multiobjective transit network design and frequency setting problem (TNDFSP) seeks to find optimal routes and their associated frequencies to operate public transport services in an urban area. The objective is to redesign the public transport network to minimize passenger costs without incurring massive changes to its former composition. The proposed TNDFSP model includes a route generation algorithm (RGA) that generates newlines in addition to the existing lines to serve the most demanding trips, and passenger assignment (PA) and frequency setting (FS) mixed-integer programming models that distribute the demand through the modified bus network and set the optimal number of buses for each line. Computational experiments were conducted on a test network and the network comprising the Royal Borough of Kensington and Chelsea in London.
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Data Availability Statement
All data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work is a result of project DynamiCITY: Fostering Dynamic Adaptation of Smart Cities to Cope with Crises and Disruptions (NORTE-01-0145-FEDER-000073) supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). Dr. K. Gkiotsalitis was funded by the Dutch Organization for Health Research and Development (ZonMw) under the L4 project “COVID 19 Wetenschap voor de Praktijk” (10430042010018).
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Journal of Transportation Engineering, Part A: Systems
Volume 149 • Issue 4 • April 2023
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© 2023 American Society of Civil Engineers.
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Received: Dec 6, 2021
Accepted: Nov 14, 2022
Published online: Feb 13, 2023
Published in print: Apr 1, 2023
Discussion open until: Jul 13, 2023
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- Junjun Li, Hao Dong, Xuedong Zhao, Hao Tang, Aimin Yin, Ruchen Xue, A transit network design and frequency setting model with graph neural network and deep reinforcement learning, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023), 10.1117/12.3003828, (40), (2023).