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How to Improve Urban Intelligent Traffic? A Case Study Using Traffic Signal Timing Optimization Model Based on Swarm

Xiancheng Fu¹, Hengqiang Gao¹, Hongjuan Cai²

¹School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China.

Sensors (Basel, Switzerland)

|April 30, 2021

Summary

This study optimizes urban intersection traffic signal timing using swarm intelligence algorithms. The new system reduces average vehicle delay by 10.25% and stops by 24.55%, improving traffic flow.

Keywords:

intersection labor division real-time control traffic signal timing

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Area of Science:

Traffic Engineering
Artificial Intelligence
Operations Research

Background:

Traffic congestion at urban intersections is a significant societal problem, leading to increased delays and reduced efficiency.
Intersections are critical nodes in urban transportation networks, often experiencing severe traffic bottlenecks.

Purpose of the Study:

To develop an intelligent traffic signal timing optimization model for urban intersections.
To reduce vehicle delays, minimize stops, and enhance overall intersection traffic capacity.

Main Methods:

Utilized swarm intelligent algorithms to create an optimization model for traffic signal timing.
Incorporated real-time traffic flow data for adaptive signal adjustments.
Conducted simulation experiments using MATLAB to evaluate the model's performance.

Main Results:

Achieved a 10.25% reduction in average vehicle delay time.
Decreased the average number of vehicle stops by 24.55%.
Increased total intersection traffic capacity by 3.56% compared to traditional methods.

Conclusions:

The proposed swarm intelligence-based traffic signal timing optimization is effective in alleviating urban intersection congestion.
Intelligent signal control significantly improves traffic flow efficiency and capacity.
This approach offers a viable solution for managing urban traffic pressure at intersections.