Development of Dynamic Traffic Control Based on Congestion Level Using IoT

Urban traffic congestion is a growing trend that leads to problems like pollution, prolonged delays, and vital risks, particularly for ambulances and fire engines. Primarily, traditional traffic management systems with fixed time-based traffic signals do not accommodate the dynamic nature of traffic, exacerbating these issues. Our project introduces an intelligent traffic light system powered by IoT sensors and Artificial intelligence algorithms to counter these situations as and when they arise. The system comprises two principal components namely: 1) infrared and radar sensors which keep watch over traffic intensity on various lanes, dynamically altered signal timings for alleviating the jam and 2) paving the way for ambulances by making automatic adjustments of the stream of road traffic to let them travel rapidly and securely. In as far as sensor processing data is involved, microcontrollers like STM32 are employed whilst Python and AI-driven algorithms take the role of decision-making. This is not only a way of alleviating urban transport congestion but also enhances road safety on the network leading towards smart sustainable cities.