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Efficient and robust data augmentation for trajectory analytics: a similarity-based approach

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Abstract

Trajectories between the same origin and destination (OD) offer valuable information for us to better understand the diversity of moving behaviours and the intrinsic relationships between the moving objects and specific locations. However, due to the data sparsity issue, there are always insufficient trajectories to carry out mining algorithms, e.g., classification and clustering, to discover the intrinsic properties of OD mobility. In this work, we propose an efficient and robust trajectory augmentation approach to construct sizeable qualified trajectories with existing data to address the sparsity issue. The high-level idea is to concatenate existing trajectories to reconstruct a sufficient number of trajectories to represent the ones going across the OD pair directly. To achieve this goal, we first propose a transition graph to support efficient sub-trajectories concatenation to tackle the sparsity issue. In addition, we develop a novel similarity metric to measure the similarity between two set of trajectories so as to validate whether the reconstructed trajectory set can well represent the original traces. Empirical studies on a large real trajectory dataset show that our proposed solutions are efficient and robust.

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Acknowledgments

Sibo Wang was supported by CUHK Direct Grant No. 4055114. He was also supported by the CUHK University Startup Grant No. 4930911 and No. 5501570.

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Authors and Affiliations

  1. University of Queensland, Brisbane, Australia

    Dan He, Boyu Ruan & Xiaofang Zhou

  2. The Chinese University of Hong Kong, Shatin, Hong Kong

    Sibo Wang

  3. Huazhong University of Science and Technology, Wuhan, China

    Bolong Zheng

  4. Institute of Electronic and Information Engineering of UESTC in Guangdong, Dongguan, China

    Xiaofang Zhou

  5. Guangzhou University, Guangzhou, China

    Xiaofang Zhou

Authors
  1. Dan He
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  2. Sibo Wang
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  3. Boyu Ruan
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  4. Bolong Zheng
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  5. Xiaofang Zhou
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Correspondence to Sibo Wang.

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He, D., Wang, S., Ruan, B. et al. Efficient and robust data augmentation for trajectory analytics: a similarity-based approach. World Wide Web 23, 361–387 (2020). https://doi.org/10.1007/s11280-019-00695-9

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  • DOI: https://doi.org/10.1007/s11280-019-00695-9

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