Identified of Collision Alert in Vehicle Ad hoc based on Machine learning

Traffic crashes and fatalities in school zones are common in urban areas. Vehicular Ad-hoc Networks (VANETs) can help prevent school zone traffic accidents by the delivery of warning messages to cars. Vehicle collision alert is a cost-effective method of managing traffic and reducing vehicle accidents. The use of roadside units to reduce automobile congestion in school zones is the aim of this essay (RSUs). It consists of three parts: one for managing collision alerts, another for clustering messages, and yet another for analyzing the cluster based on major and minor messages depending on the airbag sense data and vehicle lifetime. Using machine learning methods, this strategy collects, filters, and clusters communications. The K-means algorithm groups messages according to vehicle position, accident region, messages type, and collision type. The simulation findings reveal that when compared to earlier collision alarm systems, the proposed technique significantly improves the traffic management system.