Automated Traffic Rules and Regulations Control System

The existing traffic rules automation faces limitations in adapting to changing traffic conditions and accounting for unpredictable human behavior and as well as an absence of adequate integration of numerous elements into a single automated traffic system Methods: Through advanced image processing and computer vision, the system focuses on employing integrated system using advanced deep learning model like Inception V3 to detect two-wheelers carrying more than two adults and captures their number plate information for identification and potential enforcement actions. It also incorporates robust helmet detection mechanisms and enforcing helmet regulations effectively. Lastly, the system features an intelligent accident detection and information dissemination module, enabling prompt communication of accident data to the nearest next junction for swift emergency response and traffic management. The AI Machine Learning Automated Traffic Rules and Regulations System serves as an example of how AI may enhance urban transportation efficiency and safety. It seeks to build a more intelligent and clean system for next generations by collaborating with emerging technology. The main objective is to drastically reduce traffic offenses by imposing severe penalties. Current systems typically concentrate on individual functions, including helmet identification or tracking triple riding. Our goal is to identify people who break traffic laws by identifying not only those who ride their bikes with three or wear helmets, but also those who are engaged in accidents. To make sure quick action is done, our system will notify neighboring stations of impending danger. By addressing many factors, this strategy increases traffic law enforcement's efficacy and encourages safer driving habits and it’s observed that accuracy is increased by 96.2%