  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.
