使用100个配送摩托车滑板车自然骑行研究来导出骑行风险前体
Eunsol Cho1, Yujeong Yun2, Cheol Oh3
1Hanyang University Erica Campus, Department of Smart City Engineering, Ansan 15588, 55 Hanyangdaehak-ro, Sangnok-gu, Republic of Korea.
Accident; analysis and prevention
|June 27, 2023
概括
配送司机面临的风险增加,因为紧张的截止日期,导致更多的碰撞. 这项研究使用传感器数据确定了关键的骑行行为,以提高交付摩托车的交通安全.
科学领域:
- 交通安全 交通安全 交通安全
- 摩托车动力学 摩托车动力学
- 风险评估 风险评估
背景情况:
- COVID-19大流行加速了韩国配送服务的增长,增加了配送摩托车车祸的数量.
- 紧张的交货时间限制导致危险的骑行行为和交通事故.
- 尽管行业的增长,目前交付摩托车的交通安全措施是不够的.
研究的目的:
- 为了确定前体检测有风险的骑行事件在配送摩托车骑手.
- 了解骑行特征,用于科学和自动安全监控.
- 制定有效的措施,提高交付摩托车滑板车的交通安全.
主要方法:
- 使用GPS和惯性测量单元 (IMU) 传感器收集自然骑行数据.
- 从骑行特征数据中提取了各种候选前体.
- 使用决策树模型来分类不安全和正常的骑行事件,并优先考虑前体.
主要成果:
- 在识别危险骑行事件时,获得了95.7%的分类准确度.
- 确定了三个突出的风险前体:角速度规范,加速和X轴角速度.
- 这些发现为开发自动化安全监控系统提供了基础.
结论:
- 了解特定的骑行特征对于改善交付摩托车滑板车安全至关重要.
- 开发的模型和确定的前体可以增强安全管理系统的科学基础.
- 这项研究有助于减少交通事故,提高快速增长的配送行业的乘客安全.
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