DESIGN AND IMPLEMENTATION OF A SMART TRAFFIC LIGHT CONTROL SYSTEM

Urban traffic congestion is a persistent issue in developing nations, largely due to outdated, fixed-time traffic light systems that cannot adapt to dynamic traffic conditions. These legacy systems contribute to significant delays, fuel wastage, and driver frustration. This project addresses these challenges by designing, implementing, and testing a Smart Traffic Light Control System that uses a cost-effective and adaptive approach. The implemented system is a prototype for a four-way intersection, utilizing an ESP32 microcontroller as the central processing unit. Infrared (IR) sensors are employed to detect real-time vehicle density and waiting times in each lane. An adaptive traffic signal control algorithm, based on fuzzy logic, processes this sensor data to dynamically adjust signal timings. The system's intelligence lies in its ability to assign priority and green light duration to lanes based on the level of congestion, moving away from rigid, pre-set cycles. To ensure reliability, a fail-safe mechanism was integrated to handle potential sensor malfunctions by reverting to a fixed-time mode. The performance of the smart traffic light system was evaluated against a traditional fixed-time controller under various simulated traffic scenarios. Under high, unbalanced traffic conditions, the fuzzy logic system demonstrated a 66.7% reduction in average lane wait time and a remarkable 170% increase in total vehicle throughput. In low and balanced traffic, it achieved a 77% reduction in average lane wait time. The fail-safe mechanism was also successfully tested, confirming the system's resilience. This study concludes that the smart traffic light system offers a significant improvement over the conventional method. It presents a scalable, affordable, and practical solution for mitigating urban traffic congestion, particularly in resource-constrained environments. Recommendations for future work include integrating more robust sensors, developing an emergency vehicle pre-emption feature, and expanding the system to a networked model for city-wide traffic coordination.