IoT-Based Traffic Management System

Abstract

Rapid urbanization in developing cities has intensified road congestion, increased travel delays, and elevated carbon emissions, underscoring the need for intelligent and adaptive traffic control systems. This study presents the design and evaluation of an Internet of Things (IoT) and Artificial Intelligence (AI) - based traffic management framework developed for urban intersections in Lusaka, Zambia. The proposed system integrates ESP32-based sensor networks for real-time vehicular data acquisition, a cloud-driven processing infrastructure (Firebase), and a locally hosted AI engine employing a Random Forest Regressor for adaptive signal optimization. The system supports both automated and manual traffic control through a responsive Next.js dashboard with a latency below two seconds. Experimental simulations across three intersections revealed a 44% reduction in average vehicle waiting time, a 27% improvement in throughput, and a 27% decrease in estimated fuel consumption. These findings demonstrate the framework’s capacity to enhance mobility efficiency, reduce congestion-related emissions, and promote sustainable urban transport. The paper contributes a scalable, low-cost, and context-aware smart traffic management model adaptable to the infrastructure realities of developing cities. All experiments were conducted in a hardware-in-the-loop simulation environment using benchtop signal heads; no on-road trials with live vehicle traffic were performed.