Enhancing Road Safety Through Ai: A Comprehensive Analysis of Accident Prevention Strategies

Abstract

This paper focuses on integrating AI to improve road safety by designing an AI approach, Random Forest Classifier, for estimating the severity of accidents. By leveraging a comprehensive dataset containing variables such as weather conditions, speed, and traffic flow, the study involves several key methodologies: parsing the data, feature extraction, model testing, and tuning. The developed model got an accuracy equal to 85% and had satisfactory performance indicators with the required precision of 82%, the recall value of 78%, and the F1 score of 80%. Based on these findings, it can be concluded that the Random Forest model is able to pinpoint and categorise the level of accidents, which leads to a better tool for enhancing the safety of roads. In the final phase of this study which is the deployment phase, the trained model was serialized and saved as random_forest_model.pkl of traffic forecasting and analysis which can be applied directly to traffic signal control systems. The integration of this AI model into operational frameworks enables the prevention of accidents that have the potential of occurring as well as improving on the traffic flow by benefitting from real-time data to provide predictions. It becomes clear that the development of AI technologies to a certain extent can reveal the prospects for not only foreseeing the severity of the accident but also assessing the measures introduced in the field of safety. Possible directions for future research will be the improvement of the model utilization of more samples and study of other methods of machine learning to increase the accuracy and efficiency of the operating factors.