Reverse k Nearest Neighbor Search over Trajectories - 2018


GPS allows mobile devices to continuously give new opportunities to boost our daily lives. For example, the information collected in applications created by Uber or Public Transport Authorities will be used to arrange transportation routes, estimate capacities, and proactively identify low coverage areas. During this Project, we have a tendency to study a brand new kind of query-Reverse k Nearest Neighbor Search over Trajectories (RkNNT), that will be used for route planning and capacity estimation. Given a set of existing routes D R , a group of passenger transitions D T , and a question route Q, an RkNNT query returns all transitions that take Q joined of its k nearest travel routes. To unravel the problem, we have a tendency to 1st develop an index to handle dynamic trajectory updates, therefore that the most up-to-date transition data are out there for answering an RkNNT question. Then we introduce a filter refinement framework for processing RkNNT queries using the proposed indexes. Next, we show the way to use RkNNT to solve the optimal route coming up with problem MaxRkNNT (MinRkNNT), that is to look for the optimal route from a begin location to an end location that could attract the most (or minimum) variety of passengers primarily based on a predefined travel distance threshold. Experiments on real datasets demonstrate the potency and scalability of our approaches. To the simplest of our information, this can be the primary work to review the RkNNT downside for route coming up with.

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