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Exploring representations for optimising connected autonomous vehicle routes in multi-modal transport networks using evolutionary algorithms.

Han, Kate; Christie, Lee A.; Zavoianu, Alexandru-Ciprian; McCall, John A.W.

Authors

Kate Han



Abstract

The past five years have seen rapid development of plans and test pilots aimed at introducing connected and autonomous vehicles (CAVs) in public transport systems around the world. While self-driving technology is still being perfected, public transport authorities are increasingly interested in the ability to model and optimize the benefits of adding CAVs to existing multi-modal transport systems. Using a real-world scenario from the Leeds Metropolitan Area as a case study, we demonstrate an effective way of combining macro-level mobility simulations based on open data with global optimisation techniques to discover realistic optimal deployment strategies for CAVs. The macro-level mobility simulations are used to assess the quality of a potential multi-route CAV service by quantifying geographic accessibility improvements using an extended version of Dijkstra's algorithm on an abstract multi-modal transport network. The optimisations were carried out using several popular population-based optimisation algorithms that were combined with several routing strategies aimed at constructing the best routes by ordering stops in a realistic sequence.

Citation

HAN, K., CHRISTIE, L.A., ZAVOIANU, A.-C. and MCCALL, J.A.W. 2024. Exploring representations for optimising connected autonomous vehicle routes in multi-modal transport networks using evolutionary algorithms. IEEE transactions on intelligent transportation systems, [online], Early Access. Available from: https://doi.org/10.1109/TITS.2024.3374550

Journal Article Type Article
Acceptance Date Feb 24, 2024
Online Publication Date Mar 22, 2024
Deposit Date Feb 26, 2024
Publicly Available Date Apr 5, 2024
Journal IEEE transactions on intelligent transportation systems
Print ISSN 1524-9050
Electronic ISSN 1558-0016
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Peer Reviewed Peer Reviewed
DOI https://doi.org/10.1109/TITS.2024.3374550
Keywords Multi-modal public transport; Macroscopic simulations; Reachability isochrones; Evolutionary algorithms
Public URL https://rgu-repository.worktribe.com/output/2255352

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HAN 2024 Exploring representation for optimizing (AAM) (1.5 Mb)
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© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.





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