Urbanshield: Integrated Vehicle Security and Road Safety System For Metropolitan City

Abstract

Urbanization and the introduction of technologies had a lot to change in the transport systems, some improvements, and some problems. Numbers of vehicles have increased exponentially, leading to more accidents, increased traffic rules violation, and an increase in vehicle theft. This results in outdated manual verification, delayed response of enforcement agencies, and fragmented databases no longer manageable. This research work proposes a very intelligent mechanism integrated with real-time processing, automation, and AI-based security mechanisms to improve the safety and management of vehicle identification and the road at large. Key features of this system include automated vehicle validation from the registered license plates, an accident prediction system, IoT enhanced security mechanism, and machine learning-based pothole detection. The core aspect also consists of real-time number plate identification and tracking. It uses machine learning to eliminate misidentification, unlike conventional OCR-based systems that are typically used, in detecting stolen vehicles, unauthorized access, and traffic violations. This effective streamlined fine processing is known to be able to detect unregistered vehicles and produce instant alerts for security improvements in law enforcement. It mainly focuses on road safety improvement through ML and IoT sensors to sense and report traffic congestion, accident-prone areas on roads, as well as the conditions of the roads. The automatic pothole detection coupled with GPS mapping would send immediate reports to the municipal authorities, who would then perform the necessary maintenance timely, thus reducing the risks of accidents. The real-time alerts inform these commuters of hazardous situations and of roadworks as they navigate this space with more safety. The future work would develop a user interface that would allow non-resident Indians to control their vehicle security remotely, including providing an automated emergency response mechanism. Real-time citizen reporting would also help traffic law enforcement and road monitoring. The outcome of the research showcases the effect of merging real-time automation with machine learning in urban transport to benefit the safety and security of urban mobility and traffic management in making mobility even smarter and more sustainable.