Reimagining Public Transport with Diverse Autonomous Vehicles

This PhD project will develop intelligent, data-driven methods for integrating diverse autonomous vehicles, such as buses, shuttles, pods, cars and micromobility vehicles, into public transport networks. The research will investigate how heterogeneous fleets can be dynamically routed, scheduled and repositioned in response to changing passenger demand, traffic conditions and public transport schedules, while accounting for differences in vehicle capacity, speed, energy use and service roles. It will explore spatiotemporal data management, optimisation and AI-based coordination to improve first- and last-mile connectivity, minimise waiting times and empty travel, and ensure equitable service across different communities. The proposed methods will be evaluated through realistic urban simulations and digital twins, with the broader aim of enabling efficient, sustainable and adaptive multimodal transport systems.