Optimized Sensor Orientation for Density Based Traffic Light Control

The rising number of vehicles on roads has led to frequent traffic congestion, causing delays and fuel wastage. Traditional traffic light systems operate on a fixed time schedule, which is often inefficient during peak traffic hours. To address this issue, in this paper, the project proposes a density-based traffic light control system using IR sensors, Raspberry Pi, and LEDs. The proposed system uses IR sensors to measure the density of vehicles at each junction in real time. The Raspberry Pi processes the data and dynamically adjusts the duration of green lights for each lane based on traffic density. By prioritizing lanes with higher vehicle density, the system minimizes congestion and optimizes traffic flow. The proposed system utilized different orientations of the sensors to accurately detect the vehicles and optimize the traffic based on it. The proposed solution is cost-effective, energy-efficient, and scalable, making it suitable for urban and semi-urban areas. After numerous tests with a variety of geometric orientations, pilot results confirm that parallel orientation significantly enhances the proposed system’s performance and provides improved traffic optimization to effectively reduce the environmental impact of the idling vehicles. The systems algorithm processed the detection of vehicle density, iteratively raising the time complexity to O(n), which was then reduced to O (1).