在交通冲突建模中解决数据不平衡和复杂交互的问题:超图和生成人工智能方法
Kaiming Guan1, Junyi Zhang1, Wei Ye1
1School of Transportation, Southeast University, Nanjing, China.
Accident; analysis and prevention
|December 9, 2025
概括
这项研究通过改进的二维碰撞时间 (2D-TTC) 度量和先进的机器学习来增强交通冲突预测. 车辆速度是关键预测因素,导致高度准确的冲突检测.
科学领域:
- 交通安全工程 交通安全工程
- 机器学习应用 机器学习应用
- 智能运输系统 智能运输系统
背景情况:
- 当前的交通冲突模型与不平衡的数据和动态交互作斗争.
- 现有模型的局限性影响了一般化和现实世界的适用性.
- 需要强大的方法来预测各种交通冲突模式.
研究的目的:
- 开发一个增强的交通冲突预测模型.
- 改进在交通冲突分析中处理不平衡的数据集.
- 确定影响交通冲突的关键特征.
主要方法:
- 使用了增强的二维碰撞时间 (2D-TTC) 度量与车辆交互关系.
- 采用低采样和超采样技术,包括具有自我注意力的生成对抗网络.
- 对比了各种机器学习和深度学习模型,重点关注超图注意力网络 (HGAT) 与沙普利增量解释 (S-HGAT).
主要成果:
- 改进后的模型获得了94.21%的F1得分,从仅使用低样本的76.35%显著改善.
- 该S-HGAT模型展示了卓越的学习能力.
- 车辆的速度被确定为最有影响力的因素;前六个特征集产生了F1得分的98.41%和97.66%的准确性.
结论:
- 拟议的方法有效地解决了数据不平衡,并提高了交通冲突预测的准确性.
- S-HGAT模型和已识别的关键特征为智能运输系统提供了强大的方法.
- 这些发现对改善道路安全和交通管理有重大影响.
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