Evaluation of Fuzzy Logic-Based Adaptive Traffic Light Control System for Intelligent Highways

This study investigates the implementation of a fuzzy logic traffic control system to enhance urban traffic management. The primary purpose is to address the challenges of congestion and inefficiency associated with traditional fixed-time controllers. The study identifies key problems, such as inadequate response to fluctuating traffic conditions and long waiting times at intersections. To achieve the objectives, a comprehensive methodology was developed, incorporating real-time traffic data to inform fuzzy logic decision-making. The procedures involved analysing vehicle counts, queue lengths, and waiting times to optimise signal timings dynamically. Results indicated that the fuzzy logic system achieved a vehicle throughput of 10 to 30 vehicles, significantly higher than the fixed-time controller's throughput of 8 to 16 vehicles. Additionally, the fuzzy logic controller demonstrated a remarkable clearance rate of 99% to 100%, compared to the fixed-time controller's performance. The study's findings advocate for the adoption of fuzzy logic in traffic management policies, emphasising the need for integrating advanced technologies such as IoT and machine learning for smarter urban environments. By providing a framework for dynamic traffic control, this research contributes valuable insights into optimising traffic flow and reducing congestion, ultimately promoting more efficient and sustainable urban transportation systems.