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

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

doi:10.1145/3449726.3459476

Cited by 2 publications

(1 citation statement)

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“…Building on our initial findings regarding CAV route optimisation [9] and subsequent feedback from transport policy officers, in the present study, we demonstrate an effective way of combining macro-level mobility simulations with multi-objective evolutionary algorithms (MOEAs) [6] to discover realistic optimal trade-offs related to CAV-serviced PT routes.…”

Section: Introductionmentioning

confidence: 69%

On Discovering Optimal Trade-Offs When Introducing New Routes in Existing Multi-modal Public Transport Systems

Han

Christie

Zăvoianu

et al. 2022

Lecture Notes in Computer Science

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While self-driving technology is still being perfected, public transport authorities are increasingly interested in the ability to model and optimise the benefits of adding connected and autonomous vehicles (CAVs) to existing multi-modal transport systems. We propose a strategy that combines multi-objective evolutionary algorithms with macrolevel mobility simulations based on publicly available data (i.e., Open Street Maps data sets and transit timetables) to automatically discover optimal cost-benefit trade-offs of introducing a new CAV-centred PT service to an existing transport system. The insightful results we obtained on a real-life case study aimed at improving the average commuting time in a district of the Leeds Metropolitan Area are very promising and indicative of our strategy's great potential to support efficient data-driven public transport planning.

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Section: Introductionmentioning

confidence: 69%