Causal Analysis and Classification of Traffic Crash Injury Severity Using Machine Learning Algorithms
利用机器学习分析和分类交通事故伤害严重程度AI 解读 ↓
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利用机器学习分析和分类交通事故伤害严重程度
- 研究问题
- 如何分析州际公路事故严重程度的相关因素，并利用不同机器学习模型预测严重伤亡、较轻伤害和仅财产损失三类事故？
- 方法与思路
- 作者使用得克萨斯州六年事故数据，结合 Granger 因果分析与决策树、随机森林、XGBoost 和深度神经网络开展因素分析和分类。
- 材料陈述的贡献
- 摘要报告若干交通与环境因素较重要，且不同分类器在各严重度类别中表现不同；深度神经网络在最稀少的严重伤亡类别表现最好。
- 阅读关注点
- 核对 Granger 分析的数据时间组织、滞后设定与解释边界，并检查类别不平衡处理、训练测试划分和各类预测指标是否充分。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未获核实；Granger 关系本身不确立干预因果效应，摘要也未提供分类指标或具体数值。
原文摘要
Causal analysis and classification of injury severity applying non-parametric methods for traffic crashes has received limited attention. This study presents a methodological framework for causal inference, using Granger causality analysis, and injury severity classification of traffic crashes, occurring on interstates, with different machine learning techniques including decision trees (DT), random forest (RF), extreme gradient boosting (XGBoost), and deep neural network (DNN). The data used in this study were obtained for traffic crashes on all interstates across the state of Texas from a period of six years between 2014 and 2019. The output of the proposed severity classification approach includes three classes for fatal and severe injury (KA) crashes, non-severe and possible injury (BC) crashes, and property damage only (PDO) crashes. While Granger Causality helped identify the most influential factors affecting crash severity, the learning-based models predicted the severity classes with varying performance. The results of Granger causality analysis identified the speed limit, surface and weather conditions, traffic volume, presence of workzones, workers in workzones, and high occupancy vehicle (HOV) lanes, among others, as the most important factors affecting crash severity. The prediction performance of the classifiers yielded varying results across the different classes. Specifically, while decision tree and random forest classifiers provided the greatest performance for PDO and BC severities, respectively, for the KA class, the rarest class in the data, deep neural net classifier performed superior than all other algorithms, most likely due to its capability of approximating nonlinear models. This study contributes to the limited body of knowledge pertaining to causal analysis and classification prediction of traffic crash injury severity using non-parametric approaches.