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Related Experiment Video

Updated: Jun 13, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

A novel fuzzy system-based genetic algorithm for trajectory segment generation in urban global positioning system.

Xiaojuan Ran¹, Naret Suyaroj², Worawit Tepsan²

¹International College of Digital Innovation, Chiang Mai University, Chiang Mai 50200, Thailand; School of Information and Engineering, Sichuan Tourism University, Chengdu 610100, China.

Journal of Advanced Research

|June 8, 2025

Summary

This study introduces an enhanced Fuzzy System-based Genetic Algorithm (FGA) for automated urban trajectory generation using Global Positioning System (GPS) data. The FGA method optimizes clustering and trajectory reconstruction without manual intervention.

Keywords:

Automatic clustering Fuzzy genetic algorithm GPS data analysis Least squares regression Trajectory generation

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Area of Science:

Geographic Information Science
Computer Science
Transportation Engineering

Background:

Global Positioning System (GPS) data is crucial for urban planning and travel analysis.
Traditional trajectory generation methods lack automation and optimality due to manual cluster number settings.

Purpose of the Study:

To develop an automated trajectory segment generation method using an enhanced Fuzzy System-based Genetic Algorithm (FGA).
To overcome limitations of manual cluster number settings in traditional trajectory generation.

Main Methods:

Integration of a fuzzy system with a genetic algorithm to dynamically adjust parameters.
Utilizing angle-based partitioning and cosine-constrained segmentation for sub-trajectory generation.
Employing global search capability and least squares regression for trajectory reconstruction.

Main Results:

The FGA method automatically determines optimal cluster numbers across various clustering algorithms (K-means, K-median, FCM).
Achieved globally optimal, smooth trajectory representations with improved clustering quality, continuity, and stability.
Demonstrated effectiveness in reconstructing real-world taxi GPS data.

Conclusions:

The proposed FGA provides an effective and adaptive solution for urban GPS trajectory segment generation.
Future research will focus on enhancing scalability, noise robustness, and rule generalization.