Predictive Modeling for Smart Traffic Systems: Harnessing IoT Data Insights

The paper discusses the development of an IoT-based smart traffic system that utilizes real-time data and machine learning algorithms to enhance traffic safety, reduce congestion, and improve emergency response times. The exponential growth of vehicles on roads has increased traffic congestion, accidents, and environmental pollution, necessitating a smarter traffic management system. The paper reviews several studies demonstrating the potential of IoT-based traffic management solutions in improving traffic flow, enhancing safety, and reducing environmental impact. Furthermore, creating a sustainable and efficient urban environment is possible by integrating IoT-based smart traffic systems with other smart city solutions, such as smart energy and waste management. Chi-squared tests and regression models were employed to extensively analyze various variables in this study, including accident severity, weather conditions, and sunrise/sunset times. The findings rejected the null hypothesis, indicating strong associations among these factors. Furthermore, the residual frequency graph and regression analysis demonstrated the model's data fit and ability to capture predictor-response relationships. This research offers valuable insights into traffic accident causes and underscores the potential for real-time implementation to enhance traffic management and safety measures in smart cities.