The Anatomy of Harm: A Machine Learning Smart Shield for Predicting Highway Worker Injuries

Abstract

Despite growing interest in the application of machine learning (ML) for accident prediction and safety analysis, limited research has explored its use in predicting anatomical injury risk among highway workers. This study addresses this gap by developing a predictive model capable of classifying body parts most susceptible to injury in highway-related incidents. Positivism and interpretivism set the theoretical foundations for this study. The sequential exploratory mixed method adopted involved the preprocessing of accident datasets, feature selection and model evaluation using established performance metrics. A Support Vector Machine (SVM) algorithm was employed as the primary classifier, with its performance benchmarked against three comparative models: Naïve Bayes (NB), Random Forest (RF) and a Recurrent Neural Network (RNN). Analysis results showed that variables such as ‘region’, ‘site/project’, ‘event type’, ‘vehicles involved’ and ‘location’ were very significant in predicting bodily injuries. Moreover, the findings also indicate that the SVM model, when optimally tuned, yields competitive classification accuracy, with RF and RNN models showing promising supplementary performance. This study introduces a novel framework for body-part injury classification within high-risk highway environments tailored for highway workers. This is the first study to use real life datasets specifically collected from highway worker injuries and departs from previous studies which have focused on drivers, pedestrians and the road only.