Real-Time Vehicle Detection and Road Condition Prediction for Smart Urban Areas

In order to improve urban transportation and infrastructure maintenance in smart cities, this research tackles the need for effective vehicle recognition and road condition prediction. The research builds a hybrid model that uses both classic image processing methods and convolutional neural networks (CNNs) to reliably identify and quantify automobiles in live video streams. By analyzing visual indications and historical data, the programme reliably monitors traffic flow and assesses road conditions, achieving a 95% recognition rate and counting accuracy. These predictive skills enable timely interventions for road repair, thereby reducing maintenance costs and accident risks. To achieve the safety, efficiency, and sustainability goals of smart cities, the model incorporates real-time traffic monitoring with predictive analytics. This integration provides useful information for infrastructure development and traffic management. Innovative, data-driven solutions to improve urban mobility and infrastructure resilience in modern cities are possible, according to this research.