Efficient kNN Search with Occupation in LargeScale Ondemand RideHailing The intelligent ridehailing systems, e.g., DiDi, Uber, have served as essential travel tools for customers, which foster plenty of studies for the locationbased queries on road networks. Under the large demand of ridehailing, the nonoccupied vehicles might be insufficient for newcoming 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 Dijkstrabased algorithm for the AkNN query. Then we improve the solution by developing a gridbased DestinationOriented index, derived from GLAD 9, for the occupied and nonoccupied moving objects. Accordingly, we propose an efficient gridbased expandandbound algorithm for the approachable kNN search and conduct extensive experiments on realworld data. The results demonstrate the effectiveness and efficiency of our proposed solutions.

Collective Mobile Sequential Recommendation: A Recommender System for Multiple Taxicabs Mobile sequential recommendation was originally designed to find a promising route for a single taxicab. Directly applying it for multiple taxicabs may cause an excessive overlap of recommended routes. The multitaxicab recommendation problem is challenging and has been less studied. In this paper, we first formalize a collective mobile sequential recommendation problem based on a classic mathematical model, which characterizes timevarying influence among competing taxicabs. Next, we propose a new evaluation metric for a collection of taxicab routes aimed to minimize the sum of potential travel time. We then develop an efficient algorithm to calculate the metric and design a greedy recommendation method to approximate the solution. Finally, numerical experiments show the superiority of our methods. In tracedriven simulation, the set of routes recommended by our method significantly outperforms those obtained by conventional methods.