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Partition-based range query for uncertain trajectories in road networks

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Abstract

Query processing for trajectory data is a hot topic in the field of moving objects databases (MODs). Most of the previous research work focused on the Euclidean space, and the uncertain trajectories are represented as sheared cylinders. However, in many applications (e.g. traffic management), the movements of moving objects (MOs) are constrained by the road network environments, which makes the previous methods ineffective. In this paper, we firstly construct an uncertain trajectory model, which is composed of a sequence of segment units with earliest arrival time and latest departure time, based on the assuming availability of a maximum speed on each road segment. Secondly, we present a partition-based uncertain trajectory index (PUTI) to facilitate the search of possible MOs within the space and time range in the road networks based on the uncertain trajectory model. It provides appropriate groups to gather segment units of trajectories according to their network distances. Finally, an efficient algorithm for range query is proposed by leveraging the index. The experiments on two datasets demonstrate that the uncertain trajectory model is effective, and PUTI also significantly outperforms the network distance based MON-tree on range query.

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  1. http://www.oracle.com/technetwork/products/berkeleydb/overview/index.html

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Acknowledgments

This work was funded by the Ministry of Industry and Information Technology of China (No.2010ZX01042-002-003-001), Natural Science Foundation of China (Nos. 60703040, 61332017), and Science and Technology Department of Zhejiang Province (Nos. 2007C13019, 2011C13042, 2013C01046).

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  1. College of Computer Science, Zhejiang University, Hangzhou, 310027, People’s Republic of China

    Ling Chen, Yanlin Tang, Mingqi Lv & Gencai Chen

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  1. Ling Chen
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  2. Yanlin Tang
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  4. Gencai Chen
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Correspondence to Ling Chen.

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Chen, L., Tang, Y., Lv, M. et al. Partition-based range query for uncertain trajectories in road networks. Geoinformatica 19, 61–84 (2015). https://doi.org/10.1007/s10707-014-0206-6

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