GeoPrune: Efficiently Finding Shareable Vehicles Based on Geometric Properties

On-demand ride-sharing is rapidly growing due to its benefits of convenience and low price. Matching trip requests to vehicles efficiently is critical for the service quality. Generally, an approach that matches requests with vehicles first identifies those vehicles that could be matched through a pruning step, and then selects among these the optimal one(s) in a selection step. The pruning step is crucial to reduce the complexity of the selection step and to achieve a highly efficient matching process. In this paper, we propose an efficient and effective pruning algorithm called GeoPrune. GeoPrune exploits the geometric properties of the waiting time constraints and detour time constraints of the trip requests, which can be computed and updated efficiently. Experiments on real-world datasets show that GeoPrune reduces the number of potential vehicles by more than a factor of ten and the update cost by two to three orders of magnitude compared to state-of-the-art algorithms.