  On-demand ride-sharing is rapidly growing.Matching trip requests to vehicles
efficiently is critical for the service quality of ride-sharing. To match trip
requests with vehicles, a prune-and-select scheme is commonly used. The pruning
stage identifies feasible vehicles that can satisfy the trip constraints (e.g.,
trip time). The selection stage selects the optimal one(s) from the feasible
vehicles. The pruning stage is crucial to reduce the complexity of the
selection stage and to achieve efficient matching. We propose an effective and
efficient pruning algorithm called GeoPrune. GeoPrune represents the time
constraints of trip requests using circles and ellipses, which can be computed
and updated efficiently. Experiments on real-world datasets show that GeoPrune
reduces the number of vehicle candidates in nearly all cases by an order of
magnitude and the update cost by two to three orders of magnitude compared to
the state-of-the-art.
