评估汽车追随驾驶风格对交通冲突风险的影响,使用不对称行为模型和可解释的机器学习
Xiao-Chi Ma1, Yun-Hao Zhou2, Jian Lu3
1Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing 211189, China; Jiangsu Province Collaborative Innovation Centre of Modern Urban Traffic Technologies, Southeast University, Nanjing 211189, China; School of Transportation, Southeast University, Nanjing 211189, China; School of Civil and Environmental Engineering, Nanyang Technological University, 639798, Singapore.
Accident; analysis and prevention
|December 25, 2024
概括
这项研究引入了使用非对称行为理论的汽车追随驾驶风格 (CFDS) 的新指标. 该指标有效地预测了交通冲突风险,显示积极的司机面临更高的风险.
科学领域:
- 交通安全研究 交通安全研究
- 运输工程 运输工程 运输工程
- 行为分析行为分析.
背景情况:
- 汽车追随驾驶方式 (CFDS) 显著影响交通冲突风险.
- 现有的CFDS评估指标缺乏清晰性和解释性.
- 了解驾驶员的行为对于预测和减轻交通冲突至关重要.
研究的目的:
- 为汽车追随驾驶风格 (CFDS) 开发一个改进的,可解释的指标.
- 量化CFDS与交通冲突风险之间的关系.
- 为交通风险预测和干预策略提供理论支持.
主要方法:
- 基于非对称行为 (AB) 理论提出了改进的CFDS指标.
- 使用可解释的机器学习模型进行回归分析.
- 使用雷达-摄像头集成设备收集高精度车辆轨迹数据.
主要成果:
- 拟议的CFDS指标在数据集中显示出一致的高度重要性,与冲突风险有很强的相关性.
- 沙普利的附加解释揭示了驾驶风格对风险水平的大部分单调影响.
- 部分依赖情节表明一个复杂的,形的风险曲线,有侵略性和"伪怯"的司机在某些情况下显示出更高的风险.
结论:
- 开发了通过案例分析验证的清晰可解释的CFDS评估指标.
- 新的指标有效量化了司机的攻击性及其与冲突风险的联系.
- 为交通安全分析和主动干预提供了增强的理论支持.
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