This study addresses the critical need for effective traffic signal control in reducing traffic congestion, enhancing road safety, and minimizing environmental impacts. It proposes a real-time dynamic traffic signal control system that incorporates multi-agent systems and considers vehicle type differences. Through comprehensive evaluation experiments, the method proves highly effective in reducing delay time. Experiment 1 optimizes the split control method parameter α in a straightforward setting, while Experiment 2 extends these findings to a complex 4 × 4 intersection, emphasizing the impact of vehicle type differences. Experiment 3 assesses physical decentralization for multiple intersections, showcasing effective split calculations despite occasional execution time fluctuations. The results demonstrate the proposed method’s substantial advantages over static control and methods neglecting vehicle type, exhibiting a notable 5.8% reduction in average delay time and a 12.9% decrease in the average number of waiting times. Despite occasional execution time fluctuations in Experiment 3, the method underscores its adaptability to diverse traffic conditions and configurations, offering promising prospects for advancing traffic signal control strategies.
Optimizing Traffic Flow: A Multi-agent Approach to Dynamic Signal Control Accounting for Vehicle Types
Communic.Comp.Inf.Science
International Conference on Optimization and Data Science in Industrial Engineering ; 2023 ; Istanbul, Türkiye November 16, 2023 - November 17, 2023
26.01.2025
16 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Multi-Agent Traffic Control , Real-time Signal Timing , Traffic Signal Optimization , Vehicle Type Considerations , Urban Traffic Management Artificial Intelligence , Computer Science , Machine Learning , Information Systems and Communication Service , Cyber-physical systems, IoT , Data Structures and Information Theory , Professional Computing
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