通过机器学习进行皮卡撞车严重性分析:为发展中国家提供政策见解
Chamroeun Se1, Thanapong Champahom2, Sajjakaj Jomnonkwao3
1Institute of Research and Development, Suranaree University of Technology, Nakhon Ratchasima, Thailand.
概括
机器学习模型预测了交通事故的严重程度,XGBoost在多类伤害预测方面表现出色. 不同的模型和伤害分类影响了准确性,揭示了单车与多车碰撞的不同风险因素.
科学领域:
- 道路交通安全 道路交通安全
- 机器学习在运输中的应用
- 交通事故分析 交通事故分析
背景情况:
- 在分析皮卡车撞车严重程度方面存在方法上的差距.
- 了解单车与多车车祸的不同风险因素至关重要.
- 机器学习为改善碰撞预测提供了潜力.
研究的目的:
- 评估机器学习模型来预测碰撞严重程度.
- 比较单车和多车事故的结果.
- 确定影响事故严重性的关键风险因素.
主要方法:
- 比较后勤回归,随机森林,XGBoost和深度神经网络.
- 使用K折交叉验证和贝叶斯优化进行模型调整.
- 使用SHAP用于模型解释性和特征分析.
主要成果:
- 在两种撞击类型中,XGBoost在多类伤害分类方面表现出卓越的性能.
- 随机森林和深度神经网络分别在单辆和多辆车撞车的二进制分类中显示出强度.
- 确定了影响两种事故类型的四车道道路,未照明的道路和障碍物等共同因素.
结论:
- 模型选择和伤害分类方案显著影响预测性能.
- 不同的因素影响单车 (例如,疲劳,道路类型) 与多车辆碰撞 (例如,车辆参与,碰撞类型) 的严重程度.
- 减少单车撞车严重性的特定因素不适用于多车撞车.
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