A Smart City Framework for Real-Time Traffic Monitoring and Accident Detection

Abstract

Smart city transport networks must be highly adaptive, meaning they can quickly adjust to new road conditions. This research aims to provide a smart city architecture that can detect accidents and track traffic in realtime using edge-cloud computing, deep learning-based video analytics, and IoT sensing. In order to correctly analyse traffic and detect accidents, the platform continuously gathers heterogeneous data from roadside cameras and automobile sensors, performs essential analytics at the edge to decrease latency, and runs robust cloud analytics. The software is able to do precise traffic analyses and detect accidents because of this. Abnormal traffic event spatial and temporal patterns are captured using a mixed deep learning architecture employing recurrent neural networks and convolutional neural networks. Also, for proactive traffic management, a module that forecasts traffic patterns can be used. The proposed system exhibits low response time, robustness under varying traffic and lighting conditions, and outstanding detection accuracy, according to the experimental results. The system is both scalable and inexpensive, and it improves urban mobility, response times to emergencies, and road safety.