开发一个分析路线的算法:使用新手和老司机的案例研究
Siyao Zhu1, Theresa J Chirles2, Joel A Keller3
1College of Civil Engineering, Nanjing Tech University, Nanjing, Jiangsu 211800, China; Department of Civil and Systems Engineering, Johns Hopkins Whiting School of Engineering, Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD 21218, USA.
Journal of safety research
|September 9, 2024
概括
研究人员开发了一种新的基于GPS的算法,以量化驾驶路线的多样性和熟悉性. 这种方法有助于了解驾驶行为如何与所有年龄段的司机的身体和心理结果有关.
科学领域:
- 运输安全研究 运输安全研究
- 人与计算机的互动.
- 行为心理学 行为心理学
背景情况:
- 量化驾驶员对道路的熟悉度对于事故风险评估至关重要.
- 目前评估驾驶熟悉度的现有方法是有限的.
- 驾驶员的熟悉程度会影响驾驶安全和驾驶行为.
研究的目的:
- 开发和验证一种用于量化驾驶路线多样性和熟悉性的新方法.
- 评估驾驶行为,身体和心理结果之间的关系.
- 为了解不同年龄段的驾驶安全提供一个工具.
主要方法:
- 利用基于智能手机的DrivingApp数据,包括GPS,从新手和老司机.
- 开发了一个基于GPS数据的算法,使用同一路线行程 (SRT) 阵列识别独特的驾驶路线 (UR).
- 使用统计值,通用线性模型 (GLM) 和调整宽度首次搜索进行算法优化.
主要成果:
- 该算法在识别独特路线时实现了高精度 (0.93) 和准确性 (0.91).
- 量化驾驶多样性 (UR数量) 和基于路线的熟悉性.
- 在年轻的新手驾驶员中确定了不同的驾驶群体,在老年驾驶员中确定了 UR 和健康指标之间的显著相关性.
结论:
- 路线的多样性和熟悉性是现有的驾驶安全措施的宝贵补充.
- 驾驶行为,路线多样性和熟悉性与身体和心理结果有关.
- 开发的算法为了解驾驶员行为及其对安全和福祉的影响提供了一种新的方法.
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