A Spatiotemporal Thermo Guidance Based Real-Time Online Ride-Hailing Dispatch Framework

Abstract

Online ride-hailing platforms can gather travel requests and allocate service vehicles to balance transportation demands and supplies, which may result in an increase in the utilization rate of service resources and improve the transportation efficiency and social welfare. An effective and flexible dispatching strategy can significantly reduce the waiting time of passengers and increase the profit of service vehicles. In this paper, we propose a real-time service vehicle dispatching framework in the context of large-scale online ride-hailing, which considers all the main issues involved in the problem in a unified and integrated way. Firstly, we formally define the problem and introduce a novel mathematical model based on the spatiotemporal thermo to guide the dispatching process so as to achieve a better balance of demands and supplies. Secondly, we propose an effective machine learning method to predict the spatiotemporal thermo based on the historical data of multi-dimensional factors. Afterward, decision rules to dynamically segment timeframes and a Kuhn-Munkres based algorithm are developed to solve the dispatching problem. Finally, extensive experiments on large-scale real-world instances are conducted to verify the effectiveness of the proposed framework.