AI-Driven Smart Road Monitoring: A Case Study of Bilaspur Chhattisgarh

India’s rapid urbanization has significantly increased the strain on road infrastructure, leading totraffic congestion, poor road conditions, and higher accident rates. Bilaspur, Chhattisgarh, a growingcommercial and transportation hub, faces similar challenges. This study explores the implementation of SmartRoad Sensing Techniques (SRST) in Bilaspur to enhance real-time monitoring and road management. Theproposed system integrates IoT sensors, Artificial Intelligence (AI), GPS tracking, and cloud computing tomonitor traffic density, road deterioration, and accident-prone areas.A lab-based experimental analysis was conducted to validate the efficiency of smart road sensors in detectingpotholes, road surface friction, and traffic load impact. The results demonstrated that accelerometer-basedsensors could detect potholes with 95% accuracy, while friction analysis showed that asphalt roads degradefaster than concrete roads under heavy traffic loads. Field surveys on NH-130 and urban intersections likeSatyam Chowk indicate that peak-hour congestion, potholes, and lack of predictive maintenance are majorissues affecting commuters. The research highlights how SRST can improve traffic flow, reduce accidents, andprovide cost-effective road maintenance solutions by integrating technology-driven interventions.By implementing real-time data analysis, local authorities can make better policy decisions and improve urbanmobility and economic productivity. The lab experiments validate that smart road sensing can increase roadlifespan, reduce maintenance costs by 20%, and improve road safety by 15-20%.