对基于替代措施的安全指数进行调查,以预测在信号交叉点发生的受伤事故
Maryam Hasanpour1, Bhagwant Persaud1, Robert Mansell1
1Department of Civil Engineering, Toronto Metropolitan University, Toronto, Canada.
Traffic injury prevention
|September 26, 2024
概括
这项研究引入了一种机器学习安全指数来分类交通冲突,改善了在信号交叉点发生严重伤害事故的预测. 新方法准确地根据严重程度对冲突进行分类,有助于采取积极的安全措施.
科学领域:
- 交通安全工程 交通安全工程
- 机器学习应用 机器学习应用
- 运输系统分析 运输系统分析
背景情况:
- 有信号的十字路口是关键基础设施,严重伤害事故的发生率很高,特别是那些涉及左转车辆和通过交通的对立车辆.
- 传统的碰撞预测方法往往缺乏细节性来区分冲突严重程度,限制了主动安全干预.
- 准确的交通冲突分类对于制定有效的安全管理策略至关重要.
研究的目的:
- 开发和验证基于机器学习的安全指数,用于在信号交叉点分类交通冲突.
- 改进事故发生频率的估计,特别关注致命和严重伤害 (FSI) 事故.
- 为积极的交通安全分析提供一个实际的框架.
主要方法:
- 利用自编码器神经网络整合视频衍生的交通冲突的频率和严重性指标,生成异常分数.
- 开发了回归模型,将十字路口碰撞 (总和FSI) 与机器学习安全指数分类的冲突联系起来.
- 为实际应用定义异常分数级别之间的界限的衍生方程.
主要成果:
- 纳入分类极端冲突的回归模型显著优于使用总冲突的模型,特别是在FSI崩时.
- 机器学习整合方法在根据严重程度对冲突进行分类方面表现出准确性,较高的异常分数与较高的撞车严重程度相关.
- 拟议的安全指数有效地对交通冲突进行了分类,为交叉路口安全提供了更细致的理解.
结论:
- 开发的框架通过使用机器学习来根据异常分数对冲突进行分类,为交通安全提供了方法上的进步.
- 导出方程为缺乏资源开发自己的分类模型的司法管辖区提供了可访问的近似值.
- 这种方法增强了在信号交叉点主动估计车祸的可能性,特别是严重的车祸.
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