Machine learning based adaptive traffic prediction and control using edge impulse platform

Traffic congestion and delays are two major challenges in modern vehicle traffic control systems. These issues can be mitigated through an efficient and autonomous traffic scheduling system. The objective of the proposed methodology is to automate the traffic control system based on the density of vehicles approaching to the traffic signal without any human intervention. Unlike the conventional traffic signal systems that rely on preset timers which is often unsuitable for unpredictable traffic conditions. Therefore, the proposed approach dynamically adjusts signal timings based on real-time data. The methodology utilizes proximity sensors strategically placed at a predetermined distance from the traffic signal to detect approaching vehicles. The speed and density of vehicles are monitored based on the readings from these sensors. A Edge-Impulse-based machine learning model is proposed to predict the density and arrival time of the vehicles to the traffic signal. Using machine learning algorithms, the system can forecast future traffic conditions and optimize real-time traffic control by significantly reducing congestion and delays. Moreover, by automating the traffic scheduling process, the proposed methodology can help to reduce human error and improve the safety of road users. The proposed methodology has the potential to transform existing traffic control systems, making them more intelligent, efficient, and autonomous. The model is rigorously tested and validated to ensure its reliability and accuracy in real-world traffic scenarios.