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Comparative Evaluation of Fuzzy Logic and Q-Learning for Adaptive Urban Traffic Signal Control
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Comparative Evaluation of Fuzzy Logic and Q-Learning for Adaptive Urban Traffic Signal Control
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Journal Article
Comparative Evaluation of Fuzzy Logic and Q-Learning for Adaptive Urban Traffic Signal Control
2025
Overview
In recent years, the number of vehicles in cities has visibly increased, leading to continuous modifications in general mobility. Pollution levels and congestion cases are reaching higher numbers as well, pointing to a need for better optimization solutions. Several existing control systems still rely on fixed timings for traffic lights, lacking an adaptive approach that can adjust the timers depending on real-time conditions. This study aims to provide a design for such a tool, by implementing two different approaches: Fuzzy Logic Optimization and an Adaptive Traffic Management strategy. The first controller involves Fuzzy Logic based on rule-based that adjust green and red-light timings depending on the number of vehicles at an intersection. The second model provides traffic adjustments based on external equipment such as road sensors and cameras, offering dynamic solutions tailored to current traffic conditions. Both methods are tested in a simulated environment using SUMO (Simulation of Urban Mobility). They were evaluated according to key efficiency indicators, namely average waiting time, lost time per cycle, number of stops per intersection, and overall traffic fluidity. Results demonstrate that Q-learning maintains consistent waiting times between 2.57 and 3.71 s across all traffic densities while achieving Traffic Flow Index values above 85%, significantly outperforming Fuzzy Logic, which shows greater variability and lower efficiency under high-density conditions.
Publisher
MDPI AG
Subject
/ Learning
