Rapid urbanization and the continuous increase in vehicular traffic have made conventional traffic signal systems inefficient, leading to congestion, increased travel time, and higher fuel consumption. Traditional fixed-timing traffic lights fail to adapt to real-time traffic conditions, resulting in unnecessary delays even when certain lanes have minimal or no traffic.This project proposes a Sub-Smart Traffic Signal Control System, an intelligent yet cost-effective solution designed to optimize traffic flow using real-time data. The system utilizes sensors such as infrared (IR), ultrasonic, or camera-based modules to detect vehicle density on different lanes. Based on the collected data, the signal timing is dynamically adjusted, giving priority to lanes with higher traffic density while reducing idle time for less congested routes.Additionally, the system can be extended to include emergency vehicle detection, enabling automatic signal clearance for ambulances and fire services. The proposed solution aims to minimize traffic congestion, reduce fuel wastage, and improve overall road efficiency without the high infrastructure costs associated with fully smart traffic systems.The implementation demonstrates how a semi-automated ("sub-smart") approach can significantly enhance traffic management in developing urban areas, making it a practical and scalable solution for modern cities.
Pawar et al. (Thu,) studied this question.