Development of a Smart Traffic Management System for Nsukka Township Roads

Traffic congestion in Nsukka town has worsened significantly with increasing vehicular density, resulting in travel delays, fuel wastage, and road safety concerns. This study develops a smart traffic management system for key intersections in Nsukka, integrating vehicle detection sensors, adaptive signal control, and a centralized monitoring dashboard. The system employs inductive loop simulation via IR sensors, an Arduino Mega controller for signal timing adjustment, and a web-based traffic monitoring dashboard accessible to local traffic authorities. The adaptive signal algorithm dynamically adjusts green-phase durations based on real-time vehicle queue length estimates derived from sensor data. A prototype was installed at a simulated four-way intersection in the UNN Computer Engineering laboratory, with traffic scenarios modeled after peak-hour data collected through direct observation at two Nsukka intersections. Results indicate that the adaptive system reduced average vehicle waiting time by 38 percent compared to fixed-cycle signal control across simulated peak-hour scenarios. Signal cycle efficiency improved by 29 percent. The dashboard provided real-time queue visualization with less than two seconds of data latency. The study concludes that sensor-driven adaptive traffic management systems are technically feasible and address documented congestion problems in Nsukka. Collaboration with the Enugu State Ministry of Works and Transport for real-world deployment is recommended.