AI Based Smart Traffic Management

Abstract

A smart city's traffic management system is one of its key components. Road traffic is frequently congested due to the metropolitan cities' rapid population expansion and urban mobility. In this work, a smart traffic management system utilizing the Internet of Things (IoT) is presented to address a variety of challenges for managing traffic on roadways and to aid authorities in efficient planning. Road traffic flow is optimized using a hybrid strategy (a combination of centralized and decentralized), and diverse traffic scenarios are effectively managed using an algorithm. In order to achieve this, the system adjusts traffic signals after receiving input on traffic density from cameras, sensors, and other devices. To reduce traffic congestion, a different artificial intelligence-based method is utilized to forecast future traffic density. Additionally, during a traffic jam, RFIDs are utilized to give priority to emergency vehicles like ambulances and fire brigade vehicles. Smoke sensors are also a component of this system to detect a fire on the road. A prototype is created that not only improves traffic flow but also connects adjacent rescue departments with a centralized server to show the usefulness of the proposed traffic management system. Additionally, it harvests helpful data that is presented in graphical formats, which could aid the government in future road design. Key Words: Traffic control, Traffic light system, Traffic management, Object detection Intelligent transport systems, Smart surveillance, Computer Vision, YOLO, Machine Learning.