URoad: An Efficient Algorithm for Large-Scale Dynamic Ridesharing Service

Nowadays, although there exists many ridesharing services and dynamic matching algorithms for passengers and drivers, there is no service or algorithm that can balance the benefit of passengers and drivers while taking their time and cost constraints into consideration. In this paper, we try to solve the dynamic ridesharing problem by considering all above factors for all the participants. To this end, we present URoad, an efficient algorithm for large-scale dynamic ridesharing service, where a new price cost model is carefully designed to make up for the shortcomings of existing algorithms, and in the meantime a corresponding efficient matching algorithm is proposed to satisfy both the time and cost constraints of passengers and drivers. Specifically, for a given passenger, URoad will find out the optimal driver who can satisfy all the constraints of the passenger and the driver with the minimum detour distance. We design a series of data structures to speed up URoad for large scale ridesharing service application, e.g., Time Index, Grid Index and Greedy Strategy. Through extensive experiments, we prove that URoad can find the optimal driver for a given passenger from more than one hundred thousand drivers within 0.5 second in average.