Abstract
K nearest neighbor (kNN) search is an important problem 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 for kNN 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 of kNN query on time-dependent road networks, we propose a novel voronoi-based index in this paper. Moreover, we propose an algorithm for pre-processing 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.
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Acknowledgments
This work is supported by the grant of the National Natural Science Foundation of China No. 61402323, 61572353 and the Australian Research Council Discovery Grant DP130103051.
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Leng, M., Yang, Y., Wang, J., Hu, Q., Wang, X. (2018). Finding the K Nearest Objects over Time Dependent Road Networks. In: Cai, Y., Ishikawa, Y., Xu, J. (eds) Web and Big Data. APWeb-WAIM 2018. Lecture Notes in Computer Science(), vol 10988. Springer, Cham. https://doi.org/10.1007/978-3-319-96893-3_25
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DOI: https://doi.org/10.1007/978-3-319-96893-3_25
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