A cooperative strategy for optimizing vehicle relocations and staff movements in cities where several carsharing companies operate simultaneously

Carsharing has become a popular travel mode owing to its convenience of use, easy parking, and low cost of using a car by those who only need it occasionally. However, because of the inadequate location of carsharing stations (station-based systems) or vehicles (free-floating systems), effectively requiring expensive and complex relocation strategies, a number of customers are lost, and some carsharing companies are facing bankruptcy. This study proposes a data-driven, dynamic, multi-company relocation method, which aims to reduce relocation costs and increase profit in one-way carsharing station-based systems through cooperative strategies. The method starts from the prediction of carsharing inflows and outflows at each station throughout the day using a new deep learning algorithm designated as "the attention-enhanced temporal graph convolutional network ". It adopts an encoder-decoder structure to simultaneously capture the temporal and spatial carsharing usage patterns. A two-phase integer programming model is proposed to optimize the process of vehicle relocation and staff rebalancing with cooperative relocation strategies: the sharing of relocation staff, the sharing of vehicles and stations among the different companies. An adaptive large neighborhood search based heuristic approach is implemented to solve the two-phase model. Based on the 6-month travel records from four car sharing companies operating simultaneously in Fuzhou, China, the proposed model and cooperative strategies are assessed. The results show that the total profit of the four carsharing companies can be increased by 25.49% with the cooperation of staff and vehicles. In addition, we prospect the future relocation with automated vehicles, whereby the profit can be increased by 46.69% without the need to employ the relocation staff.