On discovering optimal trade-offs when introducing new routes in existing multi-modal public transport systems.

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 macro-level 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