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Finding Structurally and Temporally Similar Trajectories in Graphs

Abstract : The analysis of similar motions in a network provides useful information for different applications like route recommendation. We are interested in algorithms to efficiently retrieve trajectories that are similar to a given query trajectory. For this task many studies have focused on extracting the geometrical information of trajectories. In this paper we investigate the properties of trajectories moving along the paths of a network. We provide a similarity function by making use of both the temporal aspect of trajectories and the structure of the underlying network. We propose an approximation technique that offers the top-k similar trajectories with respect to a query trajectory in an efficient way with acceptable precision. We investigate our method over real-world networks, and our experimental results show the effectiveness of the proposed method. Acknowledgements We are in debt with Ioanna Miliou for helping us with the Milan GPS dataset.
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https://hal.inria.fr/hal-02956070
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Submitted on : Friday, October 2, 2020 - 1:10:38 PM
Last modification on : Monday, December 7, 2020 - 2:16:46 PM
Long-term archiving on: : Monday, January 4, 2021 - 8:47:46 AM

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Roberto Grossi, Andrea Marino, Shima Moghtasedi. Finding Structurally and Temporally Similar Trajectories in Graphs. SEA 2020 - 18th International Symposium on Experimental Algorithms, Jun 2020, Catania, Italy. pp.1-13, ⟨10.4230/LIPIcs.SEA.2020.24⟩. ⟨hal-02956070⟩

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