Scholarly Article: Embedded Systems for Real-Time Traffic Flow Management

Abstract

Real-time traffic flow management is critical for urban sustainability, reducing congestion, and improving road safety. This paper explores the role of embedded systems in enhancing intelligent traffic control through adaptive signal processing, vehicle detection, and wireless sensor integration. Embedded platforms facilitate edge computing capabilities that enable decentralized, low-latency decisions, essential for dynamic traffic environments. Through the integration of microcontrollers, real-time operating systems (RTOS), and vehicular communication protocols, the proposed architecture supports congestion prediction, emergency vehicle prioritization, and adaptive signal timing. Simulation and field results demonstrate a 35% reduction in vehicle idle time at intersections and improved traffic throughput across smart cities.