Abstract
The class of k Nearest Neighbor (kNN) queries in spatial networks has been widely studied in the literature. All existing approaches for kNN search in spatial networks assume that the weight (e.g., travel-time) of each edge in the spatial network is constant. However, in real-world, edge-weights are time-dependent and vary significantly in short durations, hence invalidating the existing solutions. In this paper, we study the problem of kNN search in time-dependent spatial networks where the weight of each edge is a function of time. We propose two novel indexing schemes, namely Tight Network Index (TNI) and Loose Network Index (LNI) to minimize the number of candidate nearest neighbor objects and, hence, reduce the invocation of the expensive fastest-path computation in time-dependent spatial networks. We demonstrate the efficiency of our proposed solution via experimental evaluations with real-world data-sets, including a variety of large spatial networks with real traffic-data.
This research has been funded in part by NSF grant CNS-0831505 (CyberTrust) and in part from METRANS Transportation Center, under grants from USDOT and Caltrans. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. We thank Professor David Kempe for helpful discussions.
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Demiryurek, U., Banaei-Kashani, F., Shahabi, C. (2010). Efficient K-Nearest Neighbor Search in Time-Dependent Spatial Networks . In: Bringas, P.G., Hameurlain, A., Quirchmayr, G. (eds) Database and Expert Systems Applications. DEXA 2010. Lecture Notes in Computer Science, vol 6261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15364-8_36
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DOI: https://doi.org/10.1007/978-3-642-15364-8_36
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