在自雇卡车司机中发生车祸的风险:使用疲劳数据和机器学习预测模型进行流行率评估
Rodrigo Duarte Soliani1, Alisson Vinicius Brito Lopes1, Fábio Santiago2
1Federal Institute of Acre, Av. Brazil, 920 - ZIP Code: 69.903-06, Rio Branco/AC, Brazil.
Journal of safety research
|February 22, 2025
概括
自雇卡车司机面临着由于长时间工作和疲劳而增加的撞车风险. 机器学习模型可以准确预测这些风险,帮助提高安全性.
科学领域:
- 职业健康 职业健康 职业健康
- 运输安全运输安全
- 数据科学数据科学数据科学
背景情况:
- 运输业转向外包劳动力,影响自雇卡车司机.
- 延长工作时间有助于驾驶员的疲劳,并提高了碰撞的风险.
- 在被调查的司机中,使用物质 (吸烟,酒精,毒品) 的流行率很高.
研究的目的:
- 调查导致卡车驾驶疲劳和损伤的因素.
- 开发一个机器学习 (ML) 模型来预测卡车司机的交通事故风险.
- 提高卡车司机和公众的安全和福祉.
主要方法:
- 在巴西圣保罗向363名自雇卡车司机提供了全面的问卷调查.
- 收集了关于社会人口统计,健康,睡眠模式和工作条件的数据.
- 利用八个机器学习算法来预测碰撞的可能性.
主要成果:
- 司机报告说,在24小时内,在疲劳之前驾驶了14.6小时,睡了5.9小时.
- 装载/卸载等待时间对工作和休息时间的重大影响.
- 机器学习模型在卡车司机撞车事故中实现了78%至85%的预测准确率.
结论:
- 验证了准确的ML衍生模型的开发,用于预测卡车司机撞车风险.
- 调查结果支持政策制定,以改善卡车司机的安全和公共卫生.
- 强调需要解决卡车运输行业的工作条件和疲劳问题.
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