Optimizing Demand-Responsive Electric Mobility Systems Under Uncertainty

Traditional bus services often operate on fixed timetables and routes that are too expensive to run in low-density areas, resulting in an infrequent service and long passenger waiting times. Demand-responsive mobility services have emerged as viable alternatives for these areas, including door-to-door ride-hailing and stop-based bus services that adjust routes based on real-time demand. As we strive to meet climate goals, electrification of these services is essential; however, deploying electric vehicles requires mid-service recharging due to a limited range, causing fleet downtime. Mobile charging systems offer a promising solution for this by enabling flexible charging that can reduce downtime and fixed costs, while increasing charging infrastructure utilization and mitigating peak grid impact. However, this introduces new challenges in jointly optimising route planning and charging operations, given the complex interactions between uncertain passenger demand, traffic congestion, vehicle charging needs and limited resources (fleet size, mobile and fixed charging infrastructure). This project aims to address these challenges by developing novel mathematical models and algorithms to support real-time routing and charging for both ride-hailing and demand responsive semi-flexible bus systems using fixed and mobile charging technology. The outcome will provide useful tools to support service planning and real-time operation management for mobility service providers.