视觉危险模型:一种混合方法来调查道路危险事件
Harikrishna Rangam1, Sathish Kumar Sivasankaran1, Venkatesh Balasubramanian1
1RBG Labs, Department of Engineering Design, Indian Institute of Technology Madras, Chennai, Tamil Nadu 600036, India.
Accident; analysis and prevention
|March 26, 2024
概括
新的视觉危险模型通过识别行人十字路口碰撞的关键风险因素来提高行人安全. 这些模型解决了数据的局限性,为决策者和工程师提供了准确的见解.
科学领域:
- 运输安全运输安全
- 数据科学数据科学数据科学
- 交通工程是交通工程.
背景情况:
- 行人安全是一个关键的全球性问题,行人占道路死亡人数的23%.
- 现有的崩分析统计模型存在冗余特征,异质性和未观察到的特征,导致偏见的估计.
- 时空属性进一步使传统的建模方法复杂化,需要先进的方法.
研究的目的:
- 开发新的视觉危险 (VH) 模型,以克服传统撞车数据分析的局限性.
- 为了解决未观察到的异质性,数据维度,以及空间时间碰撞数据中固有的异质性.
- 为加强道路安全政策和工程实践提供数据驱动的框架.
主要方法:
- 开发了两个视觉危险 (VH) 模型:VH模型 (i) 对于未观察到的异质性和VH模型 (ii) 对于空间时空模式识别后的维度和异质性.
- 集成的特征选择以减少维度和隐性类聚类以将数据分类到最多的异质类.
- 将模型应用于印度泰米尔纳德州十年 (2009-2018) 的行人十字路口事故数据.
主要成果:
- 视觉危险模型2成功生成了五个同质子类的碰撞场景.
- 确定了导致行人死亡/严重受伤事故的统计学意义上的因素,例如高速公路,老行人以及特定的道路/交通状况.
- 证明高速公路上的行人面临82%更高的致命/严重伤害的风险.
结论:
- 开发的视觉危险模型通过解决异质性和维度性,有效地减少了撞车数据估计中的偏差.
- 这些模型为理解复杂的崩动态和识别高风险场景提供了强大的框架.
- 为工程师,研究人员和政策制定者提供可操作的见解,以制定针对性的干预措施,以改善道路安全.
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