Exploring the Determinants of Pedestrian Crash Severity Using an AutoML Approach

TL;DR

This paper applies AutoML combined with SHAP analysis to identify key factors affecting pedestrian crash severity, demonstrating an efficient, interpretable approach for traffic safety analysis using Utah data from 2010-2021.

Contribution

It introduces an AutoML-based methodology integrated with SHAP for analyzing pedestrian crash severity, enhancing predictive accuracy and interpretability over traditional methods.

Findings

Lighting conditions significantly influence crash severity

Road type and weather are key factors

AutoML improves analysis efficiency and interpretability

Abstract

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.