Machine Learning Algorithm based VANET Traffic Management System

Impact of traffic congestion on the economy and ecology are becoming a significant challenge in urban cities. A well-known area where ML-based techniques have been shown to be quite helpful for resolving a variety of problems is vehicular network analysis. The wireless communication between car nodes and infrastructure is exposed to several types of attacks. This work provides a thorough investigation of machine learning techniques in the environment of vehicular ad hoc networks (VANETs). This work addresses the various applications of learning algorithms in VANETs, such as ongoing traffic estimation, accident estimation, video-based gathering of data, and vehicle monitoring. They analyze the data using statistical methods, showing the significance of excellent traffic and communication management in VANETs. This study also discusses about the application of machine learning in traffic scheduling, signal duration optimization, and traffic congestion forecasting. Machine learning has helped VANE Ts to handle traffic better and experience less congestion. Real network data were gathered from several network backbones to test the proposed model, and finally it was discovered that the RF has performed better than expected in predicting the network traffic flow while error reduction was the primary goal.