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Searching Similar Trajectories Based on Shape

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Big Data (BigData 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1709))

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Abstract

Similarity search in moving object trajectories is a fundamental task in spatio-temporal data mining and analysis. Different from conventional trajectory matching tasks, shape-based trajectory search (STS) aims to find all trajectories that are similar in shape to the query trajectory, which may be judged to be dissimilar based on their coordinates. STS can be useful in various real world applications, such as geography discovery, animal migration, weather forecasting, autonomous driving, etc. However, most of existing trajectory distance functions are designed to compare location-based trajectories and few can be directly applied to STS tasks. In order to match shape-based trajectories, we first convert them to a rotation-and-translation-invariant form. Next, we propose a distance function called shape-based distance (SBD) to calculate the accurate distance between two trajectories, which follows an align-based paradigm. Then, to accelerate STS, we propose a trajectory representation framework based on symbolic representation to support efficient rough match. Finally, extensive experiments on two real-world datasets demonstrate the effectiveness and efficiency of our framework.

Z. Fu—Contributing authors.

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Notes

  1. 1.

    http://research.microsoft.com/en-us/projects/geolife.

  2. 2.

    https://gaia.didichuxing.com.

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Authors and Affiliations

  1. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, Sichuan, China

    Zidan Fu & Kai Zheng

Authors
  1. Zidan Fu
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  2. Kai Zheng
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Corresponding author

Correspondence to Kai Zheng .

Editor information

Editors and Affiliations

  1. Southwest Jiaotong University, Chengdu, China

    Tianrui Li

  2. Shenzhen University, Shenzhen, China

    Rui Mao

  3. Chongqing University of Posts and Telecommunications, Chongqing, China

    Guoyin Wang

  4. Southwest Jiaotong University, Chengdu, China

    Fei Teng

  5. Sichuan University, Chengdu, China

    Lei Zhang

  6. Taiyuan University of Technology, Taiyuan, China

    Li Wang

  7. Shenzhen University, Shenzhen, China

    Laizhong Cui

  8. Southwest Jiaotong University, Chengdu, China

    Jie Hu

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Fu, Z., Zheng, K. (2022). Searching Similar Trajectories Based on Shape. In: Li, T., et al. Big Data. BigData 2022. Communications in Computer and Information Science, vol 1709. Springer, Singapore. https://doi.org/10.1007/978-981-19-8331-3_1

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  • DOI: https://doi.org/10.1007/978-981-19-8331-3_1

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  • Print ISBN: 978-981-19-8330-6

  • Online ISBN: 978-981-19-8331-3

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