使用变压器和主动学习替代品进行概率性车辆速度预测和基于可靠性的山地高速公路改造设计优化
Shanchuan Yu1, Yu Chen2, Shaofei Wang3
1National Engineering and Research Center for Mountainous Highways, China Merchants Chongqing Communications Research & Design Institute Co., Ltd., Chongqing 400067, China; College of Traffic & Transportation, Chongqing Jiaotong University, Chongqing 400074, China; Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China.
Accident; analysis and prevention
|February 17, 2026
概括
这项研究引入了优化山区高速公路改造的新框架. 它使用人工智能来预测车辆的速度,通过成本有效的设计调整提高驾驶安全和可靠性.
科学领域:
- 运输工程 运输工程
- 道路交通安全 道路交通安全
- 机器学习 机器学习
背景情况:
- 传统的高速公路设计方法不足以应对山区地形中的驾驶员行为变化.
- 现有的方法可能会导致由于决定性速度假设,安全边际不足.
研究的目的:
- 开发基于可靠性的设计优化 (RBDO) 框架,用于改造山区高速公路.
- 将概率性车辆速度预测与以安全为导向的几何设计相结合.
- 为了提高驾驶安全和可靠性,同时考虑改造成本.
主要方法:
- 基于变压器的深度学习模型被开发用于基于高速公路对齐的概率性车辆速度预测.
- 使用横向稳定性,速度协调和停止视距离的随机极限状态函数量化驾驶安全可靠性.
- 积极学习的Kriging替代模型被用来有效地解决RBDO问题.
主要成果:
- 变压器模型准确地捕捉了多式联运车辆的速度分布,超过了传统的回归和循环模型.
- 纵向斜率和曲线半径的适度调整显著提高了驾驶安全可靠性.
- 拟议的RBDO框架在平衡安全改进与改造成本方面表现出有效性.
结论:
- 深度学习和基于代理的RBDO的整合为数据驱动,以安全为导向的交通基础设施设计提供了强大的方法.
- 这些发现为优化山区高速公路的几何设计提供了宝贵的见解,以提高驾驶安全和可靠性.
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