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Related Experiment Video

Updated: Sep 23, 2025

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Published on: January 20, 2023

GPS data Mining at Signalized Intersections for Congestion Charging.

Wang Yu^1,2, Zhang Dongbo³, Zhang Yu⁴

¹Key Laboratory for Traffic and Transportation Security of Jiangsu Province, Huai'an, Jiangsu China.

Computational Economics

|May 16, 2022

Summary

This study introduces an AI algorithm using GPS data to accurately estimate traffic queue lengths and identify lanes. This cost-effective method aims to make congestion charging more acceptable and implementable in cities.

Keywords:

Congestion charging Lane identification Queue length estimation Traffic shockwave

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Last Updated: Sep 23, 2025

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

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Published on: January 20, 2023

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

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Trajectory Data Analyses for Pedestrian Space-time Activity Study

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Published on: February 25, 2013

Area of Science:

Intelligent Transportation Systems
Traffic Engineering
Artificial Intelligence

Background:

Urban traffic congestion worsened by increased private car use post-COVID-19.
Current congestion charging is hampered by high monitoring costs and public acceptance issues.
Accurate real-time traffic data is crucial for effective congestion pricing.

Purpose of the Study:

To develop a cost-effective artificial intelligence (AI) model for accurate traffic congestion monitoring.
To enhance the accuracy and public acceptability of congestion charging systems.
To leverage Global Positioning System (GPS) data for real-time traffic analysis.

Main Methods:

Developed a comprehensive model integrating real-time lane identification and queue length estimation.
Utilized traffic shockwave theory and massive real-time GPS data.
Validated the model using field-collected data in Guangzhou, China.

Main Results:

The comprehensive model accurately identifies vehicle lanes at intersections.
Real-time queue length estimation achieved satisfactory accuracy.
The proposed method offers a cost-effective solution for dynamic road pricing.

Conclusions:

The AI-driven model provides accurate, real-time traffic data for congestion charging.
This approach can significantly reduce the cost of monitoring traffic congestion.
The findings support the wider implementation of dynamic congestion charging policies in urban areas.