Efficient Knn Search With Occupation In Large-Scale On-Demand Ride-Hailing

The intelligent ride-hailing systems, e.g., DiDi, Uber, have served as essential travel tools for customers, which foster plenty of studies for the location-based queries on road networks. Under the large demand of ride-hailing, the non-occupied vehicles might be insufficient for new-coming user requests. However, the occupied vehicles which are about to arrive their destinations could be the candidates to serve the requests close to their destinations. Consequently, in our work, we study the k Nearest Neighbor search for moving objects with occupation, notated as Approachable kNN (AkNN) Query, which to the best of our knowledge is the first study to consider the occupation of moving objects in relevant fields. In particular, we first propose a simple Dijkstra-based algorithm for the AkNN query. Then we improve the solution by developing a grid-based Destination-Oriented index, derived from GLAD [9], for the occupied and non-occupied moving objects. Accordingly, we propose an efficient grid-based expand-and-bound algorithm for the approachable kNN search and conduct extensive experiments on real-world data. The results demonstrate the effectiveness and efficiency of our proposed solutions.