AI Driven Urban Planning for Real Time Traffic Monitoring Framework Using OpenCV and YOLO

Abstract

Cities are increasingly struggling with the ever growing problems of traffic jams and frequent road inci- dents. Recognizing this, our work introduces a smart, real time traffic monitoring solution. We’ve built a framework that uses readily available, open source computer vision tools and sophisticated predictive analytics. This system is designed to capture high quality video and environmental readings from strategically positioned cameras throughout a city. This rich data stream then allows for a detailed, moment by moment analysis of how many vehicles are on the road, how fast they are moving, and their direction of travel. At the heart of our system is a YOLO based object detection algorithm. This enables our system to achieve impressive accuracy – consistently identifying vehicles with average precision scores between 0.78 and 0.88, and an overall accuracy ranging from 75% to 89%. This high level of performance is maintained even when conditions change due to the model training with harsh weather conditions also. To ensure the data we analyze is reliable and clean, we also employ robust image preprocessing techniques using OpenCV. Drawing inspiration from recent advancements in both research and real world applications [3]–[5], [7], our framework goes further by incorporating multiple types of environmental measurements – things like temperature, humidity, and wind speed. This broader data integration allows for dynamic adjustments to traffic signals, precise identification of traffic hotspots, and informed planning for future road capacity. Early tests of our system are promising, suggesting it could cut down on average monthly traffic detour time and potentially reduce accident rates by around. Ultimately, by seamlessly connecting with Ge- ographic Information Systems (GIS), our framework aims to give city planners actionable insights. This will empower them to improve emergency response capabilities and build more resilient transportation infrastructure for the long haul. In essence, our framework demonstrates a practical way to leverage cutting edge technology to make modern traffic systems safer, smoother, and more responsive to the needs of urban environments. Our novel Framework can be used in any region of the world as it is a plug and play framework , only prerequisite for the plug and play would be to train the model with the region specific data.