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.

通过 Automated Machine Learning (AutoML) 以及 SHAP 分析，本研究调查了行人事故严重性，提供了一种简化和易于访问的方法来分析关键因素，如照明条件、道路类型和天气等对行人事故严重程度的影响。这种 AutoML 与 SHAP 分析的整合不仅增强了预测准确性，还提供了对有效行人安全措施的关键见解，强调了这种方法在推进行人事故严重性分析方面的潜力。

基于 AutoML 方法探究行人事故严重性的决定因素

Exploring the Determinants of Pedestrian Crash Severity Using an AutoML  Approach

This study presents the first investigation of pedestrian crash severity
using the TabNet model, a novel tabular deep learning method exceptionally
suited for analyzing the tabular data inherent in transportation safety
research. Through the application of TabNet to a comprehensive dataset from
Utah covering the years 2010 to 2022, we uncover intricate factors contributing
to pedestrian crash severity. The TabNet model, capitalizing on its
compatibility with structured data, demonstrates remarkable predictive
accuracy, eclipsing that of traditional models. It identifies critical
variables, such as pedestrian age, involvement in left or right turns, lighting
conditions, and alcohol consumption, which significantly influence crash
outcomes. The utilization of SHapley Additive exPlanations (SHAP) enhances our
ability to interpret the TabNet model's predictions, ensuring transparency and
understandability in our deep learning approach. The insights derived from our
analysis provide a valuable compass for transportation safety engineers and
policymakers, enabling the identification of pivotal factors that affect
pedestrian crash severity. Such knowledge is instrumental in formulating
precise, data-driven interventions aimed at bolstering pedestrian safety across
diverse urban and rural settings.

该研究首次应用 TabNet 模型对行人事故严重程度进行调查，发现与交通安全研究相关的表格数据中的复杂因素。通过对覆盖 2010 年至 2022 年犹他州的全面数据集应用 TabNet 模型，我们揭示了对行人事故严重程度有重要影响的关键变量，如行人年龄、涉及左转或右转、照明条件和酒精消耗量。使用 SHapley Additive exPlanations (SHAP) 提高了我们解释 TabNet 模型预测的能力，确保了我们深度学习方法的透明度和可理解性。分析得出的洞察力为交通安全工程师和政策制定者提供了宝贵的指引，使其能够确定影响行人事故严重程度的关键因素。这样的知识对于在不同的城市和农村环境中制定精确的、数据驱动的干预措施以增强行人安全至关重要。