Cross Online Ride-Sharing for Multiple-Platform Cooperations in Spatial Crowdsourcing

The last few years have seen the wide applications of ride-sharing, a transportation service that allows users to share their travel routes. A typical problem for ride-sharing is to find an optimal route for each worker to serve the dynamically arriving requests with different objectives. Previous studies focus on the route planning on a single platform. However, a single platform may have an uneven distribution of supply and demand, which causes the platform to lose requests from lack of available workers. Luckily, some ride-sharing platforms provide the same service, which enables their collaborations. The inter-platform collaborations on ride-sharing can ease the worker shortages and greatly improve the service quality, but have not been studied yet. In this paper, we propose a Cross Online Ride-sharing (CORS) problem, which allows a platform to borrow the available workers from other platforms to serve its own requests. We first design two algorithms to select the optimal available worker from other platforms, ROWS and DOWS. ROWS randomly picks an available worker, while DOWS selects the optimal worker with the minimum additional travel distance calculated based on his/er predicted destination direction. Then, we design an efficient CORS framework that embeds the proposed optimal worker selection algorithms for the CORS problem. Extensive experiments on real and synthetic datasets demonstrate the effectiveness and efficiency of our algorithms.