Predictive Analytics in Smart Transportation: Integrating AI and IoT to Forecast Traffic Volumes

The integration of Artificial Intelligence (AI) and Internet of Things (IoT) technologies presents a transformative opportunity to come up with smart transportation systems. This research basically explores the application of these technologies that could help in predictive analysis of traffic volumes, thereby enhancing urban mobility management and reducing congestion. In this a predictive model has been developed using linear regression based on synthesized data which includes different variables such as time of day, day of week, and peak traffic hours due to high congestion on roads, reflecting typical urban traffic conditions. The objective is to improve the accuracy and reliability of traffic prediction by leveraging real-time data from IoT sensors and historical traffic patterns. The model's performance was evaluated through metrics such as the Mean Squared Error (MSE) and R-Squared values, with results which indicate a strong predictive capability. Additionally, error distribution analysis heighted areas for potential improvements and model refinement in future. This paper discusses the implications of our findings for traffic management systems and proposes directions for future research to optimize the hybridization of AI and IoT technologies in enhancing the adaptability and efficiency of urban transportation networks.