Design and Implementation of a Microcontroller-Based Intelligent Traffic Light Control System using Sensor-Driven Vehicle Detection

Dr D.Nageswara1 , Dr K.Chadrasekhar Reddy2

Sri Gayatri jr college, Anantapuramu

Govt Degree college, Uravakonda

ABSTRACT: Rapid urbanization and the continuous increase in vehicular population have significantly intensified traffic congestion in metropolitan and developing transportation infrastructures. Conventional fixed-time traffic light systems operate using predetermined switching intervals and generally fail to respond effectively to dynamic traffic density variations, resulting in excessive waiting time, increased fuel consumption, traffic delays, and environmental pollution. To address these limitations, this work proposes an adaptive intelligent traffic signal control system based on microcontroller-integrated sensor-driven vehicle detection using a simulation-based embedded architecture. The proposed system employs a hybrid sensing framework consisting of Infrared (IR) sensors and Ultrasonic sensors integrated with an Arduino virtual microcontroller model developed in MATLAB/Simulink. The sensor subsystem continuously monitors real-time traffic conditions across a four-way road intersection and estimates vehicle density in individual lanes. Based on the acquired traffic information, an intelligent control mechanism incorporating Finite State Machine (FSM) modeling and Adaptive Density Scheduling (ADS) dynamically allocates traffic signal durations according to road congestion levels. Unlike conventional fixed-cycle controllers, the proposed method enables real-time signal adaptation through density-aware green-time optimization and automatic traffic prioritization. The embedded controller performs state transition management for red, yellow, and green signal conditions while ensuring reliable synchronization among multiple traffic directions. The complete architecture is modeled and validated using MATLAB/Simulink virtual simulation to emulate realistic urban traffic conditions under multiple traffic scenarios including low-density, moderate-density, and heavy-congestion environments.

KEYWORDS: Adaptive Traffic Control, MATLAB/Simulink, Arduino, FSM, IR Sensor, Ultrasonic Sensor, Intelligent Transportation Systems.