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Trajectory similarity clustering based on multi-feature distance measurement

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Abstract

With the development of GPS-enabled devices, wireless communication and storage technologies, trajectories representing the mobility of moving objects are accumulated at an unprecedented pace. They contain a large amount of temporal and spatial semantic information. A great deal of valuable information can be obtained by mining and analyzing the trajectory dataset. Trajectory clustering is one of the simplest and most powerful methods to obtain knowledge from trajectory data, which is based on the similarity measure between trajectories. The existing similarity measurement methods cannot fully utilize the specific features of trajectory itself when measuring the distance between trajectories. In this paper, an enhanced trajectory model is proposed and a new trajectory clustering algorithm is presented based on multi-feature trajectory similarity measure, which can maximize the similarity of trajectories in the same cluster, and can be used to better serve for applications including traffic monitoring and road congestion prediction. Both the intuitive visualization presentation and the experimental results on synthetic and real trajectory datasets show that, compared to existing methods, the proposed approach improves the accuracy and efficiency of trajectory clustering.

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  1. http://avires.dimi.uniud.it/papers/trclust/creatle_ts2.m

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Acknowledgements

The authors would like to thank the reviewers for their useful comments and suggestions for this paper. This work was supported by the National Natural Science Foundation of China (61702010, 61672039), the Key Program for University Top Talents of Anhui Province (gxbjZD2016011), the University Natural Science Research Program of Anhui Province (KJ2017A327), and the Science and Technology Project of Wuhu City (2016cxy04).

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Authors and Affiliations

  1. School of Geography and Tourism, Anhui Normal University, Wuhu, 241003, China

    Qingying Yu & Yonglong Luo

  2. School of Computer and Information, Anhui Normal University, Wuhu, 241003, China

    Qingying Yu, Yonglong Luo & Chuanming Chen

  3. Anhui Provincial Key Laboratory of Network and Information Security, Wuhu, 241003, China

    Qingying Yu, Yonglong Luo & Chuanming Chen

  4. Department of Computer, Information Science, Engineering, University of Florida, Gainesville, FL, 32611, USA

    Shigang Chen

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  1. Qingying Yu
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  2. Yonglong Luo
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Correspondence to Yonglong Luo.

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Yu, Q., Luo, Y., Chen, C. et al. Trajectory similarity clustering based on multi-feature distance measurement. Appl Intell 49, 2315–2338 (2019). https://doi.org/10.1007/s10489-018-1385-x

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