驾驶员风险级别的识别,包括人格特征,人口特征和驾驶行为
Yu Liu1,2, Tangzhi Liu1, Xingliang Liu1
1College of Traffic & Transportation, Chongqing Jiaotong University, Chongqing, China.
Traffic injury prevention
|August 25, 2025
概括
司机的个性特征和人口统计数据显著影响了车祸风险. 机器学习模型有效地识别高风险驾驶员,多层感知器显示最高准确度以提高道路安全.
科学领域:
- 心理学
- 计算机科学
- 运输安全
背景情况:
- 人类因素对车辆事故发生率有很大影响,特别是在高危驾驶员中.
- 有效的驾驶员风险评估对于制定有针对性的安全干预和培训计划至关重要.
研究的目的:
- 开发一种可靠的方法来确定驾驶员的风险水平.
- 评估机器学习算法在预测驾驶员安全方面的有效性.
- 根据个人驾驶员培训计划的设计.
主要方法:
- 使用症状清单-90和人口统计数据对50名司机的个性特征进行定量评估.
- 根据现场测试评估的驾驶行为,对风险进行三级分类 (低,中,高).
- 开发和比较四个机器学习模型 (SVM,CART,XGBoost,MLP) 用于风险识别.
主要成果:
- 在司机风险水平和包括性别,强迫症,抑郁,焦虑,敌意和偏执思想等因素之间发现了显著的相关性.
- 这四种机器学习模型在识别高风险驾驶员方面都实现了100%的回忆.
- 多层感知子模型表现出优异的性能,准确度为86. 7%,F1得分为87. 0%.
结论:
- 人格特征和人口特征是司机风险的关键预测因素.
- 机器学习模型,特别是多层感知器,在识别有风险的司机方面具有很高的效率.
- 这些发现为提高驾驶员安全和优化培训策略提供了宝贵的见解.
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