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Updated: Jul 19, 2025

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research
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Published on: December 18, 2020

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预测和分析高速公路碰撞发生的可能性,考虑到危险的驾驶行为

Yongfeng Ma1, Junjie Zhang1, Jian Lu1

  • 1Jiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast University, Nanjing 211189, China; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing 211189, China.

Accident; analysis and prevention
|August 13, 2023
PubMed
概括

这项研究使用驾驶行为和交通数据开发了一种准确的高速公路撞车预测模型. 极端梯度提升 (XGBoost) 模型,结合危险的驾驶行为,实现了高预测准确性,提高了交通安全.

关键词:
崩的可能性预测预测.自由公路的高速公路.有风险的驾驶行为.沙普利的添加剂解释 (SHAP)在XGBoost中使用.

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