用机器学习预测交通事故死亡风险，关键影响因素可解释。
An Explainable Machine Learning Approach to Traffic Accident Fatality Prediction
- 基于达卡市2017-2022年事故数据，用多种算法分类死伤结果。
- LightGBM模型表现最佳，ROC-AUC达0.72。
- 通过SHAP分析揭示时间、地点、车辆类型等关键影响因素。
道路交通事故对全球公共健康构成重大威胁，尤其在孟加拉国等发展中国家尤为严重。构建可靠的事故后果预测模型对制定有效预防措施至关重要。本研究利用2017至2022年达卡都市区交通事故数据库，采用逻辑回归、支持向量机、朴素贝叶斯、随机森林、决策树、梯度提升、LightGBM和人工神经网络等多种机器学习分类算法，构建事故伤亡结果分类模型。为保障模型可解释性，引入SHAP（SHapley Additive exPlanations）方法，揭示影响事故致死的关键因素。结果显示，LightGBM模型性能最优，ROC-AUC达0.72。通过全局、局部及特征依赖性分析，进一步揭示伤亡类别、事故发生时间、地点、车辆类型和道路类型对致死风险具有决定性影响。研究成果为发展中国家政策制定者与道路安全从业者提供证据支持，助力制定精准减损策略。
原文摘要 · Abstract (English)
Road traffic accidents (RTA) pose a significant public health threat worldwide, leading to considerable loss of life and economic burdens. This is particularly acute in developing countries like Bangladesh. Building reliable models to forecast crash outcomes is crucial for implementing effective preventive measures. To aid in developing targeted safety interventions, this study presents a machine learning-based approach for classifying fatal and non-fatal road accident outcomes using data from the Dhaka metropolitan traffic crash database from 2017 to 2022. Our framework utilizes a range of machine learning classification algorithms, comprising Logistic Regression, Support Vector Machines, Naive Bayes, Random Forest, Decision Tree, Gradient Boosting, LightGBM, and Artificial Neural Network. We prioritize model interpretability by employing the SHAP (SHapley Additive exPlanations) method, which elucidates the key factors influencing accident fatality. Our results demonstrate that LightGBM outperforms other models, achieving a ROC-AUC score of 0.72. The global, local, and feature dependency analyses are conducted to acquire deeper insights into the behavior of the model. SHAP analysis reveals that casualty class, time of accident, location, vehicle type, and road type play pivotal roles in determining fatality risk. These findings offer valuable insights for policymakers and road safety practitioners in developing countries, enabling the implementation of evidence-based strategies to reduce traffic crash fatalities.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。