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Trajectory Data Analyses for Pedestrian Space-time Activity Study
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Novel trajectory clustering method based on distance dependent Chinese restaurant process.

Reza Arfa1,2, Rubiyah Yusof1,2, Parvaneh Shabanzadeh1,2

  • 1Centre for Artificial Intelligence and Robotics, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia.

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|April 5, 2021
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This study introduces a new trajectory analysis system that simultaneously clusters trajectories and models paths. The novel approach automatically determines the number of clusters, outperforming traditional methods in trajectory clustering tasks.

Keywords:
Anomaly detectionChinese restaurant processDistance dependent CRPPath modellingTrajectory clustering

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Area of Science:

  • Intelligent Transport Systems
  • Data Mining
  • Machine Learning

Background:

  • Trajectory clustering and path modeling are crucial for intelligent transport systems.
  • Traditional methods treat these as separate tasks, limiting performance.
  • Existing approaches often require pre-defining the number of clusters.

Purpose of the Study:

  • To develop a unified system for simultaneous trajectory clustering and path modeling.
  • To enable automatic determination of the number of clusters.
  • To improve the performance of trajectory clustering using path model knowledge.

Main Methods:

  • Utilized the distance dependent Chinese restaurant process (DDCRP).
  • Developed an integrated algorithm for joint clustering and path modeling.
  • Evaluated the method on publicly available trajectory datasets.

Main Results:

  • The proposed method demonstrated superior performance in trajectory clustering compared to traditional approaches.
  • Significant improvements were observed on both tested datasets.
  • The system successfully determined the number of clusters automatically.

Conclusions:

  • The proposed DDCRP-based method offers an effective solution for combined trajectory clustering and path modeling.
  • This approach enhances trajectory analysis by integrating path information into the clustering process.
  • The method provides a more robust and automated alternative for intelligent transport systems.