Road Speed Violation Prediction Using Machine Learning with Route-Based Weather Forecasting

The proposed Road Speed Violation Prediction System Integrated with selected Route weather prediction works effectively and also integrates with the real-time road speed, road type and weather conditions to identify unsafe driving situations with high speed in danger zones. Based on this prediction, the system generates route-based alerts as “Go slow” on the navigation interface to warn the driver. These alerts improve driver safety by informing them about changing road and environmental conditions, allowing them to adjust their speed and driving behavior according to the road type. Experimental observations tells that the system works effectively in different driving areas such as school zones, and highways. Another important feature of the system is that the ability to update warnings automatically according to the driver’s real-time location during journey. This ensures that the alerts remain relevant throughout the journey. In addition, the model can be integrated easily with existing platforms, making practical and solution for supporting safer driving practices, reducing the chances of speed-related accidents, and improving overall road safety in everyday transportation scenarios.

 [1]         L. Breiman, “Random Forests,” *Machine Learning*, vol. 45, no. 1, pp. 5–32, 2001.  [2]         T. Chen and C. Guestrin, “XGBoost: A Scalable Tree Boosting System,” *Proceedings of the 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining*, pp. 785– 794, 2016.  [3]         T. Hastie, R. Tibshirani, and J. Friedman, *The Elements of Statistical Learning: Data Mining, Inference, and Prediction*. Springer, 2001.  [4]         H. Hartenstein and K. Laberteaux, *VANET: Vehicular Applications and Inter-Networking Technologies*. Wiley, 2008.  [5]         Open Street Map Contributors, *OpenStreetMap Data*. Available: [https://www.openstreetmap.org](https://www.ope nstreetmap.org) (Accessed 2024).  [6]         J. Misener and S. E. Shladover, *Intelligent Transportation Systems: Technologies and Applications*. Springer, 2018. [7]     L. Andrews, *Smart Mobility and Public Safety: Urban Traffic Monitoring and warning Systems*. Academic Press, 2020.  [8]        D. Han, *Flood Risk Assessment and Management*. UK: Bentham Science Publishers, pp. 23–135, 2011.  [9]   R. Bishop, “A Survey of Intelligent Vehicle Applications Worldwide,” *IEEE Intelligent Transportation Systems*, vol. 1, no. 1, pp. 5–17, 2000.  [10]    S. Tsugawa, D. Gruyer, and O. Shladover, “Vehicle Automation: History, Current State, and Future Trends,” *IEEE Transactions on Intelligent Vehicles*, vol. 1, no. 1, pp. 1–19, 2016.