评估机器学习技术在预测交通事故严重性的有效性
Izuchukwu Chukwuma Obasi1, Chizubem Benson1
1European University Cyprus, Center for Risk and Safety in the Environment, Diogenis 6, Engomi, Nicosia, 2404, Cyprus.
Heliyon
|August 10, 2023
概括
机器学习模型可以准确预测交通事故的严重程度. 随机森林和物流回归实现了87%的准确性,识别了改善道路安全的关键因素,如发动机容量和驾驶员年龄.
科学领域:
- 道路交通安全问题 道路安全问题
- 交通事故分析分析
- 机器学习应用程序 机器学习应用程序
背景情况:
- 交通事故是一个主要的全球公共安全问题,造成严重的伤害和死亡.
- 准确预测事故严重程度对于实施有效的道路安全措施和减少伤亡人数至关重要.
研究的目的:
- 提出并评估使用机器学习模型预测交通事故严重性的分析框架.
- 为了比较天真贝斯,随机森林,物流回归和人工神经网络在预测事故严重性的表现.
主要方法:
- 对英国一大批交通事故数据集 (2005-2014,N=2,047,256) 的分析.
- 开发和比较四种机器学习模型:天真贝叶斯,随机森林,物流回归和人工神经网络.
- 使用随机森林进行特征重要性分析,以确定关键贡献因素.
主要成果:
- 随机森林和物流回归模型实现了87%的整体预测准确度,超过了naive bayes和人工神经网络 (80%).
- 影响事故严重性的关键因素包括发动机容量,车辆年龄,车辆制造商,驾驶员年龄,车辆机动,一天中的时间和道路等级.
- 随机森林模型在不同伤害严重程度类别中表现出卓越的预测性能.
结论:
- 拟议的机器学习框架,特别是随机森林和物流回归,提供了一个非常准确的方法来预测交通事故严重程度.
- 识别的敏感变量为有针对性的道路安全干预提供了有价值的见解.
- 该框架可适应各种数据集,为增强交通安全系统和管理提供了强大的工具.
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