  This study investigates pedestrian crash severity through Automated Machine
Learning (AutoML), offering a streamlined and accessible method for analyzing
critical factors. Utilizing a detailed dataset from Utah spanning 2010-2021,
the research employs AutoML to assess the effects of various explanatory
variables on crash outcomes. The study incorporates SHAP (SHapley Additive
exPlanations) to interpret the contributions of individual features in the
predictive model, enhancing the understanding of influential factors such as
lighting conditions, road type, and weather on pedestrian crash severity.
Emphasizing the efficiency and democratization of data-driven methodologies,
the paper discusses the benefits of using AutoML in traffic safety analysis.
This integration of AutoML with SHAP analysis not only bolsters predictive
accuracy but also improves interpretability, offering critical insights into
effective pedestrian safety measures. The findings highlight the potential of
this approach in advancing the analysis of pedestrian crash severity.
