Abstract
Knearest neighbor (kNN) search is an important problem in location-based services(LBS) and has been well studied on static road networks. However, in real world, road networks are often time-dependent; i.e., the time for traveling through a road always changes over time. Most existing methods forkNN query build various indexes maintaining the shortest distances for some pairs of vertices on static road networks. Unfortunately, these methods cannot be used for the time-dependent road networks because the shortest distances always change over time. To address the problem ofkNN query on time-dependent road networks, we propose a novel voronoi-based index in this paper. Furthermore, we propose a novel balanced tree, namedV-tree, which is a secondary level index on voronoi-based index to make our querying algorithm more efficient. Moreover, we propose an algorithm for preprocessing time-dependent road networks such that the waiting time is not necessary to be considered. We confirm the efficiency of our method through experiments on real-life datasets.
Highlights
With the rapid development of mobile devices, k nearest neighbor search on road networks has become more and more important in location-based services (LBS)[1,2,3,4]
Given a query location and a set of objects on a road network, it is to find k nearest objects to the query location. k nearest neighbor (kNN) search problem has been well studied on static road networks
When the waiting time is considered, it is more difficult to build an index for kNN query by existing methods because it is difficult to estimate an appropriate waiting time for precomputing the minimum travel time between two vertices
Summary
With the rapid development of mobile devices, k nearest neighbor (kNN) search on road networks has become more and more important in location-based services (LBS)[1,2,3,4]. All these methods precompute and maintain the shortest distances for some pairs of vertices to facilitate kNN query These indexes cannot be used for time-dependent road networks. When the waiting time is considered, it is more difficult to build an index for kNN query by existing methods because it is difficult to estimate an appropriate waiting time for precomputing the minimum travel time between two vertices. The main idea of our method is to precompute minimum travel time functions (or mtt-function for short) instead of concrete values for some pairs of vertices and design a “dynamic” voronoi-based index based on such functions. We propose a novel voronoi-based index for time-dependent road networks and an algorithm to answer kNN query using our index.
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