A Machine Learning Approach for Predicting Road Accidents

Abstract

Road accidents pose significant challenges to public safety and necessitate proactive measures to mitigate them. This paper introduces a machine-learning approach for predicting road accident incidences, leveraging diverse datasets encompassing traffic patterns, weather conditions, and historical accident records. The proposed model integrates feature engineering techniques to capture the multifaceted nature of variables influencing accidents. Through the application of advanced machine learning algorithms, such as ensemble methods and neural networks, the model aims to discern complex patterns within the data, facilitating accurate predictions of accident likelihood. The study also explores the interpretability of the model outputs, providing insights into the key predictors and their interactions. Validation and performance assessment involve rigorous testing on diverse datasets to ensure the generalizability and robustness of the predictive model. The outcomes of this research hold promise for the development of proactive road safety strategies and the implementation of targeted interventions, ultimately contributing to reducing road accidents and their associated societal impacts.