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Updated: Nov 10, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

Novel trajectory clustering method based on distance dependent Chinese restaurant process.

Reza Arfa^1,2, Rubiyah Yusof^1,2, Parvaneh Shabanzadeh^1,2

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

Peerj. Computer Science

|April 5, 2021

Summary

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 detection Chinese restaurant process Distance dependent CRP Path modelling Trajectory 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.