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Updated: Sep 13, 2025

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从连接车辆数据考虑纵向和横向危险驾驶行为的交叉路口碰撞分析:空间机器学习方法
1Department of Civil, Environmental & Construction Engineering, University of Central Florida, Orlando, FL 32816, United States.
Accident; analysis and prevention
|July 31, 2025
概括
联网车辆数据显示,分析横向转行为,如硬左转和右转,可以显著提高交叉路口碰撞预测的准确性. 这种方法考虑了驾驶模式的空间变化和非线性影响.
科学领域:
- 运输工程 运输工程
- 交通安全分析 交通安全分析
- 机器学习应用 机器学习应用
背景情况:
- 传统的交叉路口安全研究侧重于宏观层面的数据,忽视微观层面的驾驶行为.
- 联网汽车 (CV) 技术可以提取详细的驾驶动态.
- 十字路口的侧向转向行为对于安全至关重要,但尚未得到充分研究.
研究的目的:
- 通过包括纵向和横向行为,全面分析十字路口的驾驶动态.
- 在碰撞频率预测中解决空间异质性和非线性效应.
- 为了提高十字路口碰撞预测模型的准确性.
主要方法:
- 从CV数据中提取驾驶行为特征,包括纵向运动和横向转.
- 开发一种新的空间机器学习 (ML) 框架,将非线性ML模型 (LightGBM) 与地理加权回归集成.
- 训练全球和本地ML模型以捕捉平均估计和空间异质性.
主要成果:
- 包括横向转行为显著提高了交叉路口撞车频率预测的准确性.
- 拟议的整合LightGBM的空间ML框架在RMSE,MAE和R2中表现优于传统模型 (随机森林,XGBoost,LightGBM,MLP).
- 驾驶特征表现出非线性冲击和空间异质性;在城市地区和市中心地区,强制制制动和加速度对后端撞车的影响不同,而在郊区,强制左转会影响横滑和左转撞车.
结论:
- 侧向转行为是十字路口安全的关键预测因素.
- 空间机器学习框架有效地捕捉局部驾驶模式并改善碰撞预测.
- 了解驾驶行为对特定环境 (如市中心,郊区) 的非线性影响对于有针对性的安全干预至关重要.
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