Enhancing Traffic Management with Deep Learning Based Vehicle Detection and Scheduling Systems

Traffic congestion is an inescapable issue in contemporary urban life, leading to time wastage and increased accident risks. This paper presents an innovative approach that harnesses machine learning and computer vision to monitor vehicle density on roads and optimize traffic signal timings at intersections. With the escalating number of vehicles on the roads, traffic congestion has become a critical problem. Queue lengths at intersections surge with rising traffic flows, rendering traditional traffic system inefficient. In response, this paper introduces a real-time traffic light control algorithm that adapts to traffic flow. utilizing computer vision and machine learning to discern traffic flow characteristics at signalized road intersections, employing state-of-the-art real-time object detection using a deep Convolutional Neural Network known as YOLO. YOLOcan be implemented on embedded controllers through Transfer Learning, enabling deep neural network capabilities even on limited hardware resources. The proposed system comprises a camera system capturing real-time video feeds of roads, which are then processed by a machine learning model to detect vehicle density. The scheduling system dynamically adjusts traffic signal green times based on observed vehicle density. Furthermore, we conduct an in-depth analysis using various machine learning models to predict traffic patterns. This analysis encompasses a performance comparison of models like K-Nearest Neighbor, random forests, and decision trees to forecast traffic volume with respective accuary of 53.43%, 98.88%, 99.09%. The study also investigates the influence of parameters such as time of day, weather and temperature conditions. The primary objective of our system is to optimize traffic flow by minimizing waiting times at traffic signals, thereby reducing congestion and enhancing the overall commuting experience for drivers.