使用随机森林和随机参数逻辑模型预测骑自行车者撞车的伤害严重程度
Antonella Scarano1, Maria Rella Riccardi1, Filomena Mauriello1
1University of Naples Federico II Department of Civil, Architectural and Environmental Engineering Via Claudio 21, 80125 Naples, Italy.
Accident; analysis and prevention
|September 8, 2023
概括
通过了解碰撞因素,可以提高骑自行车者的安全性. 致命事故的关键预测因素包括车辆的机动和驾驶员的性别,而对于严重的伤害,自行车离开道路至关重要.
科学领域:
- 道路安全工程 道路安全工程
- 运输科学 运输科学
- 数据科学在事故分析中的数据科学
背景情况:
- 骑自行车的人面临不成比例的高道路交通伤害和死亡率.
- 了解碰撞因素对于制定有效的安全措施至关重要.
- 目前的知识差距限制了针对骑自行车安全的特定背景干预措施.
研究的目的:
- 调查影响骑自行车撞车严重程度的因素.
- 识别关键的道路,环境,车辆,司机和骑自行车者的特点.
- 为了比较机器学习和计量经济学建模用于崩分析.
主要方法:
- 在英国 (2016-2018) 分析了72,363起骑自行车事故.
- 实施随机森林 (RF) 算法,包括随机生存森林 (RSF).
- 应用随机参数逻辑模型 (RPLM) 来评估未观察到的异质性.
主要成果:
- 报告员无国界确定了致命事故 (例如,车辆机动,驾驶员性别) 和严重伤害 (例如,自行车离开车道) 的关键预测因素.
- 产生了361个死亡事故的"如果"规则和349个严重受伤事故的"如果"规则.
- RPLM揭示了影响严重性的重要因素,如骑自行车的年龄≥75,男性骑自行车的性别和驾驶员年龄55-64岁.
结论:
- 射频和RPLM提供了对骑自行车撞车严重性因素的互补见解.
- 鉴定的因素可以为针对性的安全对策提供信息.
- 这些建议旨在提高骑自行车的安全性,并促进骑自行车作为一种交通方式.
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