Utilizing Genetic-Neural System Fusion for Advanced Prediction and Classification of Damage Intensity in Road Traffic Accidents

This study proposes an analysis and prediction framework for road traffic accidents (RTAs) in the hilly terrain of Himachal Pradesh and several states in, India, integrating machine learning techniques such as neural networks (NNs) and genetic algorithms (GAs). Utilizing a comprehensive dataset comprising historical accident records, geographical features, weather conditions, and demographic information specific to Himachal Pradesh, the framework aims to identify patterns and correlations contributing to RTAs. Neural networks are employed to discern complex relationships within the data, while genetic algorithms optimize model parameters for enhanced predictive accuracy. By forecasting accident-prone areas and periods, this approach enables proactive interventions and targeted road safety measures. The outcomes of this research provide valuable insights for stakeholders and policymakers, facilitating the reduction of RTAs and mitigating their impact in the challenging terrain of Himachal Pradesh including all states of India.