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

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.