Safety Analytics

Safety analytics leverages data-driven methods, including machine learning algorithms like deep neural networks and various clustering techniques, to predict and prevent accidents, particularly in high-risk industries such as trucking and construction. Current research focuses on addressing data imbalances through techniques like oversampling and on improving model interpretability to understand the factors influencing accident risk, often incorporating driver perceptions of safety climate. These advancements hold significant potential for improving workplace safety by enabling more effective resource allocation, targeted interventions, and the development of safer systems through human-AI codesign approaches.