Traffic Signal Violation Handling Using Machine Learning

Accidents are occurring more frequently as a result of the sharp rise in traffic infractions including speeding, running red lights, and failing to wear helmets brought on by the quick increase in the number of vehicles. These issues are difficult for current traffic management systems to handle well, especially when it comes to giving emergency vehicles priority in emergency situations. The goal of this research is to use computer vision and machine learning techniques to create a reliable, automated system for identifying and managing traffic signal violations. In addition to facilitating enforcement procedures and precisely identifying offenders, the system will have a dedicated module that guarantees emergency vehicles may go quickly and unhindered across junctions. The suggested strategy is anticipated to greatly improve road safety and traffic flow by lowering infractions and speeding up emergency response times. The initiative offers a thorough method for updating traffic control and enhancing urban mobility by emphasising on promoting responsible motorists through data-driven traffic management techniques and public awareness campaigns.