一种在线适应性学习方法,用于预测多种类型的交通参与者的微观行为
Meng Li1, Tao Chen2, Hanggai Chen3
1College of Safety and Ocean Engineering, China University of Petroleum, Beijing 102249, China; Key Laboratory of Oil and Gas Safety and Emergency Technology, Ministry of Emergency Management, Beijing 102249, China.
Accident; analysis and prevention
|October 15, 2025
概括
本研究引入了一种适应性框架,用于预测交通参与者的行为,通过在线学习和错误纠正来提高可靠性. 这种新方法在新的交通场景中提高了预测准确度.
科学领域:
- 智能运输系统 智能运输系统
- 深度学习用于行为预测.
- 交通流动动态 交通流动动态
背景情况:
- 在动态运输枢纽中预测多种类型的交通参与者行为是复杂的.
- 当前的深度学习模型在线学习,错误纠正和跨场景概括方面扎.
- 由于缺乏实时校正机制,预测错误通常会持续存在.
研究的目的:
- 开发一种适应性框架,可靠地实时预测交通参与者的行为.
- 克服在线学习,错误纠正和概括现有模型的局限性.
- 在动态运输环境中提高预测准确性和可靠性.
主要方法:
- 一个适应性框架,将在线学习与概率错误校正相结合.
- 使用扩展卡尔曼波器进行实时轨迹校正.
- 采用了层次图形编码器,以实现高效的转移学习和统一的节点边缘平面建模,以实现多式联络的融合.
主要成果:
- 拟议的方法在预测未见的场景中的行为方面明显优于现有的方法.
- 使用真实世界的运输枢纽数据展示了强大的性能.
- 实现了优越的跨场景通用化,最低限度的再培训要求.
结论:
- 适应性框架为现代交通系统中的实时行为预测提供了一个有希望的解决方案.
- 在线学习和概率错误校正的整合提高了模型的可靠性.
- 该方法有效地解决了动态环境和交通参与者行为多样化的挑战.
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