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.

翻译：本研究通过自动化机器学习（AutoML）方法探究行人碰撞严重程度，提供了一种简化且易用的关键因素分析手段。基于2010-2021年犹他州的详细数据集，研究采用AutoML评估各解释变量对碰撞结果的影响。通过引入SHAP（Shapley加法解释）方法，研究揭示了预测模型中各特征的贡献度，深化了对照明条件、道路类型、天气等因素影响行人碰撞严重程度的理解。论文强调数据驱动方法的高效性与普及性，论述了AutoML在交通安全分析中的应用优势。AutoML与SHAP分析的结合不仅提升了预测精度，还增强了模型可解释性，为制定有效的行人安全措施提供了关键见解。研究结果凸显了该方法在推进行人碰撞严重程度分析方面的潜力。