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Related Experiment Video

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Trajectory Data Analyses for Pedestrian Space-time Activity Study

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Published on: February 25, 2013

Automatic Electric Tricycles Trajectory Tracking and Multi-Violation Detection.

Leishan Guo¹, Bo Yu², Benhao Xie¹

¹School of Qilu Transportation, Shandong University, Jinan 250062, China.

Sensors (Basel, Switzerland)

|August 28, 2025

Summary

This study introduces an Electric Tricycle Object Detection (ETOD) model and an Electric Tricycle Violation Detection System (ETVDS) to enhance urban traffic safety by tracking electric tricycle movements and identifying violations like speeding and illegal lane changes.

Keywords:

electric tricycles target detection trajectory tracking violation detection

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Area of Science:

Urban planning and traffic management
Computer vision and artificial intelligence
Transportation safety engineering

Background:

Electric tricycles contribute to rising traffic violations, impacting urban safety.
Automatic tracking and violation detection for electric tricycles are crucial for traffic management.

Purpose of the Study:

To develop an automated system for detecting electric tricycle violations.
To improve urban traffic safety by addressing electric tricycle-related infractions.

Main Methods:

An Electric Tricycle Object Detection (ETOD) model was created using a custom dataset.
A multi-object tracking algorithm was integrated to form an Electric Tricycle Violation Detection System (ETVDS).
The system analyzes electric tricycle trajectories to identify speeding, overloading, and illegal lane changes.

Main Results:

The ETOD model achieved real-time, accurate recognition and high-precision detection of electric tricycles.
The ETVDS successfully identified multiple violations and complex traffic conflicts involving electric tricycles.
The developed system demonstrated effectiveness in challenging urban traffic scenarios.

Conclusions:

The proposed ETOD model and ETVDS offer a robust technological solution for mitigating electric tricycle traffic violations.
This system can significantly enhance urban traffic safety and management.
The research provides a foundation for future intelligent transportation systems focused on micro-mobility safety.