A vehicle value based ride-hailing order matching and dispatching algorithm

• Published in: Engineering applications of artificial intelligence 2024-06, Vol.132, p.107954, Article 107954
• Main Authors: Shi, Bing, Xia, Yiming, Xu, Shuai, Luo, Yikai
• Format: Article
• Language: English
• Subjects: Idle vehicle dispatching; Order matching; Ride-hailing; Social welfare
• ISSN: 0952-1976
• DOI: 10.1016/j.engappai.2024.107954

Online ride-hailing has become one of the most important transportation ways. In the ride-hailing system, how to efficiently match orders with vehicles and dispatch idle vehicles are key issues. The ride-hailing platform needs to match orders with vehicles and dispatch idle vehicles efficiently to maximize social welfare. However, the matching and dispatching decisions at the current round may affect the supply and demand of ride-hailing in the future rounds since they will affect the future vehicle distributions in different geographical zones. In fact, vehicles in different zones at different times may have different values for the matching and dispatching results. In this paper, we use the vehicle value function to characterize the spatio-temporal value of vehicles in each zone and then use it to design the order matching and idle vehicle dispatching algorithm to improve the long-term social welfare. In addition, in the order matching, passengers may untruthfully report the maximum price they are willing to pay to maximize their own profits, which can affect the order matching and thus may result in the losses of the long-term social welfare. Therefore, we design a VCG based pricing algorithm to prevent the strategic behavior of passengers. We further run experiments to evaluate the proposed algorithm. The experimental results show that our algorithm can outperform the state-of-the-art algorithm in terms of social welfare by 11.7% and service ratio by 11.1%. This work can provide some useful insights for the online ride-hailing platform to design practical order matching and pricing strategies. [Display omitted] •We design a vehicle value function to optimize order matching and improve the long-term social welfare by using a reinforcement learning method.•We consider the dispatching of idle vehicles to a zone as a virtual order and process idle vehicle dispatching issue as (virtual) order matching as well.•We design a VCG based pricing method to prevent the strategic behavior of passengers and ensure positive profits for the platform.