Reverse K Nearest Neighbor Search Over Trajectories (Extended Abstract)
2018 IEEE 34TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE)(2018)
摘要
We study a new kind of query - a Reverse k Nearest Neighbor Search over Trajectories (RkNNT), which can be used for route planning and capacity estimation in the transportation field. Given a set of existing routes D-R, a set of passenger transitions D-T, and a query route Q, an RkNNT query returns all transitions that take Q as one of its k nearest travel routes. We develop an index to handle dynamic trajectory updates, so that the most up-to-date transition data is available for answering an RkNNT query using a filter-refine processing framework. Further, an application of using RkNNT to plan the optimal route in bus networks, namely MaxRkNNT, is proposed and studied. Experiments on real datasets demonstrate the efficiency and scalability of our approaches. In the future, the RkNNT can be extended and applied to the traffic prediction.
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关键词
Trajectory database,route planning,transit network,capacity prediction
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