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Effective spatio-temporal semantic trajectory generation for similar pattern group identification

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Abstract

The daily trajectories of individual movements convey a concise overview of their behaviors, with different social roles having different trajectory patterns. Therefore, we can identify users or groups based on the similar of their trajectory patterns. However, most existing trajectory analysis focuses only on the spatial and temporal analyses of the raw trajectory data and misses essential semantic information concerning behaviors. In this paper, we propose a new trajectory semantics calculation method to identify groups with similar behaviors. We first propose a fast and efficient two-phase method for identifying stay regions within daily trajectories and enriching the stay regions with semantic labels based on points of interest to generate semantic trajectories. Furthermore, we design a semantic similarity measure model using geographic and semantic similarity factors to measure the similarity between semantic trajectories. We also propose a pruning strategy using time entropy to decrease the number of complex calculations and comparisons to improve performance. The results of our extensive experiments on the real trajectory dataset of the Geolife project show that our proposed method is both effective and efficient.

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Acknowledgements

This work is supported by National Key R&D Program of China (No. 2017YFC0803300, No. 2016YFB0901200), the National Natural Science of Foundation of China (No. 61703013, 91546111, 91646201, 61803035, 71831001), the Key Project of Beijing Municipal Education Commission (No. KM201810005023, KM201810005024, KM201810037002, KZ201610005009, SZ201510005002), the Major Project of Beijing Wuzi University (No. 2019XJZD11), the Youth Fund Project of Beijing Wuzi University (No. 2018XJQN03), the Beijing Intelligent Logistics System Collaborative Innovation Center Open Project (No. BILSCIC-2019KF-05), the Grass-roots Academic Team Building Project of Beijing Wuzi University (No. 2019XJJCTD04), the Beijing Youth Top-notch Talent Plan of High-Creation Plan (No. 2017000026833ZK25), the Canal Plan-Leading Talent Project of Beijing Tongzhou District (No. YHLB2017038), the Construction Project for National Engineering Laboratory for Industrial Big-data Application Technology (No. 312000522303), the State Grid Science and Technology Project (No. 52272216002B, No. JS71-16-005) and the Open Research Fund Program of Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data.

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Authors and Affiliations

  1. School of Information, Beijing Wuzi University, Beijing, 101149, China

    Yang Cao, Fei Xue & Hengliang Tang

  2. Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

    Yang Cao, Zhiming Ding, Limin Guo & Zhi Cai

  3. College of Economics and Management, Beijing University of Technology, Beijing, 100124, China

    Yuanying Chi

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  1. Yang Cao
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Correspondence to Zhiming Ding.

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Cao, Y., Xue, F., Chi, Y. et al. Effective spatio-temporal semantic trajectory generation for similar pattern group identification. Int. J. Mach. Learn. & Cyber. 11, 287–300 (2020). https://doi.org/10.1007/s13042-019-00973-y

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