FRAMEWORK DEVELOPMENT FOR MAPPING ROAD TRAFFIC VIOLATIONS USING AI

Abstract

Traffic violations are a major global problem that leads to road safety issues, especially in developing countries. Implementing artificial intelligence (AI) and machine learning (ML) in traffic management systems represents a significant leap forward in urban infrastructure. The study conducted in Islamabad is a testament to the transformative power of these technologies. This study supports the benefit gained in terms of better adherence to traffic laws and potentially increased revenue by integrating an e-challan system to address lane-changing violations. The public's openness to data sharing, with the assurance of privacy and proper compensation, indicates a progressive attitude toward embracing such innovations. This sets the stage for more robust traffic regulations and creating a national database, which could revolutionize urban mobility and elevate safety standards to meet global benchmarks. Moreover, the application of Python for precise lane marking and vehicle detection demonstrates the practicality of the YOLOv8 model in real-world scenarios, ensuring that the traffic and incident management system becomes more efficient and proactive, leading to safer roads and a better driving experience for all. This approach aligns with public opinion and paves the way for a future where technology and civic infrastructure work hand in hand to enhance the quality of urban life.