Gap-Fuzzy Adaptive Signal Control: Enhancing Urban Traffic Efficiency

Traffic congestion in urban areas has intensified due to the rapid growth of vehicles, inadequate infrastructure planning, and unsynchronized traffic signals. This study presents an adaptive traffic signal control strategy called Gap-Fuzzy, which combines the Mamdani fuzzy logic controller with a gapout detection mechanism. The system dynamically adjusts the duration of the green light based on real-time traffic data, including queue length and arrival rate. Furthermore, the green phase is terminated early if prolonged gaps in vehicle flow are detected. We evaluated the proposed Gap-Fuzzy system using the SUMO microscopic traffic simulator. The results indicate that it reduced vehicle waiting times by up to 70% and CO2 emissions by 25% compared to fixed-time control. In addition, it outperformed the SUMO actuated controller under low, medium, and high traffic conditions while maintaining comparable performance under very high demand. These findings highlight the effectiveness of integrating fuzzy logic with gap-out detection to enhance traffic flow and minimize environmental impact.