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.

Automated summary

In this study, an integrated modeling framework is developed to coordinate the matching, rebalancing, and charging of ride-hailing EVs, aiming to improve the ride-hailing system performance. Empirical experiments demonstrate the effectiveness of the proposed framework and provide managerial insights for promoting sustainable transportation.