Exploring Factors Affecting Pedestrian Crash Severity Using TabNet: A Deep Learning Approach
This work addresses pedestrian safety for transportation engineers and policymakers, but it is incremental as it applies an existing deep learning method (TabNet) to a new dataset in transportation safety.
This study tackled the problem of predicting pedestrian crash severity by applying the TabNet deep learning model to a comprehensive dataset from Utah (2010-2022), achieving remarkable predictive accuracy that eclipsed traditional models and identifying critical factors like pedestrian age and lighting conditions.
This study presents the first investigation of pedestrian crash severity using the TabNet model, a novel tabular deep learning method exceptionally suited for analyzing the tabular data inherent in transportation safety research. Through the application of TabNet to a comprehensive dataset from Utah covering the years 2010 to 2022, we uncover intricate factors contributing to pedestrian crash severity. The TabNet model, capitalizing on its compatibility with structured data, demonstrates remarkable predictive accuracy, eclipsing that of traditional models. It identifies critical variables, such as pedestrian age, involvement in left or right turns, lighting conditions, and alcohol consumption, which significantly influence crash outcomes. The utilization of SHapley Additive exPlanations (SHAP) enhances our ability to interpret the TabNet model's predictions, ensuring transparency and understandability in our deep learning approach. The insights derived from our analysis provide a valuable compass for transportation safety engineers and policymakers, enabling the identification of pivotal factors that affect pedestrian crash severity. Such knowledge is instrumental in formulating precise, data-driven interventions aimed at bolstering pedestrian safety across diverse urban and rural settings.