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.
Exploring the Determinants of Pedestrian Crash Severity Using an AutoML Approach
International Conference on Transportation and Development 2024 ; 2024 ; Atlanta, Georgia
13.06.2024
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Exploring the determinants of pedestrian–vehicle crash severity in New York City
Online Contents | 2013
|Exploring Factors Affecting Pedestrian Crash Severity Using TabNet: A Deep Learning Approach
ArXiv | 2023
|A Hierarchical Modeling Approach to Predict Pedestrian Crash Severity
Springer Verlag | 2019
|Exploring the Effect of Visibility Factors on Vehicle–Pedestrian Crash Injury Severity
Transportation Research Record | 2023
|