Coordinating matching, rebalancing and charging of electric ride-hailing fleet under hybrid requests

Due to the potential to reduce energy consumption and greenhouse gas emission, electric vehicles have been widely adopted in ride-hailing services Besides the frequently considered immediate requests, reservation gives precise information on future requests, which may help to increase ride-hailing system performance. In this study, an integrated modelling framework is developed for coordinating the highly correlated matching, rebalancing and charging of ride-hailing EVs under hybrid requests, including both immediate and reservation requests. A heuristic algorithm is then proposed so that relatively large instances can be solved in a reasonable time. Through extensive empirical experiments constructed with real-world trip data, we show that the proposed framework is able to improve ride-hailing system performance. Managerial insights on the impacts of ratio of reservation requests, fleet size, and spatial coverage of charging infrastructures are also provided to promote sustainable transportation.