An adaptive scaled network for public transport route optimisation

Heyken Soares, Philipp; Mumford, Christine L.; Amponsah, Kwabena; Mao, Yong

Abstract

We introduce an adaptive network for public transport route optimisation by scaling down the available street network to a level where optimisation methods such as genetic algorithms can be applied. Our scaling is adapted to preserve the characteristics of the street network. The methodology is applied to the urban area of Nottingham, UK, to generate a new benchmark dataset for bus route optimisation studies. All travel time and demand data as well as information of permitted start and end points of routes, are derived from openly available data. The scaled network is tested with the application of a genetic algorithm adapted for restricted route start and end points. The results are compared with the real-world bus routes.

Citation

Heyken Soares, P., Mumford, C. L., Amponsah, K., & Mao, Y. (2019). An adaptive scaled network for public transport route optimisation. Public Transport, 11(2), 379-412. https://doi.org/10.1007/s12469-019-00208-x

• Journal Article Type: Article
• Acceptance Date: Jun 1, 2019
• Online Publication Date: Jul 29, 2019
• Publication Date: Aug 1, 2019
• Deposit Date: Jun 20, 2019
• Publicly Available Date: Jun 20, 2019
• Journal: Public Transport
• Print ISSN: 1866-749X
• Electronic ISSN: 1613-7159
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 11
• Issue: 2
• Pages: 379-412
• DOI: https://doi.org/10.1007/s12469-019-00208-x
• Keywords: Mechanical Engineering; Management Science and Operations Research; Information Systems; Transportation
• Public URL: https://nottingham-repository.worktribe.com/output/2211748
• Publisher URL: https://link.springer.com/article/10.1007%2Fs12469-019-00208-x
• Contract Date: Jun 20, 2019