Optimising the introduction of connected and autonomous vehicles in a public transport system using macro-level mobility simulations and evolutionary algorithms

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

Abstract

The past five years have seen a rapid development of plans and test pilots aimed at introducing connected and autonomous vehicles (CAVs) in public transport systems around the world. Using a real-world scenario from the Leeds Metropolitan Area as a case study, we demonstrate an effective way to combine macro-level mobility simulations based on open data (i.e., geographic information system information and transit timetables) with evolutionary optimisation techniques to discover realistic optimised integration routes for CAVs. The macro-level mobility simulations are used to assess the quality (i.e., fitness) of a potential CAV route by quantifying geographic accessibility improvements using an extended version of Dijkstra's algorithm on an abstract multi-modal transport network.

• Presentation Conference Type: Conference Paper (published)
• Conference Name: GECCO '21: Genetic and Evolutionary Computation Conference
• Start Date: Jul 10, 2021
• End Date: Jul 14, 2021
• Publication Date: Jul 8, 2021
• Deposit Date: Jan 7, 2025
• Publisher: Association for Computing Machinery (ACM)
• Pages: 315-316
• Book Title: GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference Companion
• ISBN: 9781450383516
• DOI: https://doi.org/10.1145/3449726.3459476