Exploring the Determinants of Pedestrian Crash Severity Using an AutoML Approach
It addresses pedestrian safety analysis for traffic researchers and policymakers, but is incremental as it applies existing AutoML and SHAP methods to a new dataset.
This study tackled pedestrian crash severity by using Automated Machine Learning (AutoML) with SHAP analysis on a Utah dataset from 2010-2021, resulting in improved predictive accuracy and interpretability for factors like lighting and weather.
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.