Reverse k Nearest Neighbor Search over Trajectories

• Main Authors: Wang, Sheng, Bao, Zhifeng, Culpepper, J. Shane, Sellis, Timos, Cong, Gao
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2020
• Subjects: Engineering::Computer science and engineering; Trajectory Database; Route Planning
• Online Access: https://hdl.handle.net/10356/140038
• Institution: Nanyang Technological University

GPS enables mobile devices to continuously provide new opportunities to improve our daily lives. For example, the data collected in applications created by Uber or Public Transport Authorities can be used to plan transportation routes, estimate capacities, and proactively identify low coverage areas. In this paper, we study a new kind of query-Reverse κ Nearest Neighbor Search over Trajectories (RκNNT), which can be used for route planning and capacity estimation. Given a set of existing routes DR, a set of passenger transitions DT, and a query route Q, an RκNNTquery returns all transitions that take Q as one of its κ nearest travel routes. To solve the problem, we first develop an index to handle dynamic trajectory updates, so that the most up-to-date transition data are available for answering an RκNNTquery. Then we introduce a filter refinement framework for processing RκNNTqueries using the proposed indexes. Next, we show how to use RκNNTto solve the optimal route planning problem MaxRκNNTMinRκNNT), which is to search for the optimal route from a start location to an end location that could attract the maximum (or minimum) number of passengers based on a predefined travel distance threshold. Experiments on real datasets demonstrate the efficiency and scalability of our approaches. To the best of our knowledge, this is the first work to study the RκNNTproblem for route planning.