Machine Learning for Accident Prediction and Prevention

This chapter explores how ML-based modelsincluding Random Forest, CNN, LSTM, and Reinforcement Learning frameworksare revolutionizing accident prediction and prevention across domains such as smart transportation, industrial safety, logistics, and autonomous systems. It highlights end-to-end ML workflows covering data acquisition, feature engineering, model training, real-time alert systems, and deployment using IoT and AI-integrated infrastructure. The chapter also discusses practical implementations through case studies, along with technical, ethical, and policy challenges such as data imbalance, privacy concerns, and decision accountability in autonomous systems. By integrating AI-driven predictive analytics with proactive safety interventions, ML demonstrates significant potential to reduce human error, optimize risk assessment, and enable intelligent safety ecosystems for future-ready industries and smart cities.