功能增强的iTransformer:为高精度的长视野流量预测提供了两阶段框架
Yonghui Duan1, Yucong Zhang1, Xiang Wang2
1Department of Civil Engineering, Henan University of Technology, Zhengzhou, Henan, China.
PloS one
|January 9, 2026
概括
我们介绍了功能增强的iTransformer (FE-iTransformer),这是一个新的框架,用于准确的长视界流量预测. 该模型通过增强具有丰富时空背景的输入序列来显著提高预测准确性,为智能交通系统提供可靠的无图形替代方案.
科学领域:
- 人工智能的人工智能
- 运输工程 运输工程
- 数据科学数据科学数据科学
背景情况:
- 智能运输系统 (ITS) 依赖于准确的长视界交通流量预测.
- 现有的基于变压器的模型经常将特征提取和预测结合起来,限制域信息集成.
- 交通数据中的复杂的时空依赖性给预测带来了重大挑战.
研究的目的:
- 提出一个新的两阶段框架,FE-iTransformer,用于增强流量预测.
- 通过解特征提取和预测来解决端到端模型的局限性.
- 提供一个部署准备好的,无图形的替代方案,用于ITS中的流量预测.
主要方法:
- 开发了一个特征增强模块 (FEM) 来从时空动态,周期性和时间上下文中提取全球上下文.
- 实施了一种逐步的功能增强机制,以以全球上下文向量丰富输入序列.
- 利用iTransformer的骨干来对丰富的表示进行序列预测.
主要成果:
- 在高速公路和城市数据集上的废弃研究证实了两阶段设计和FEM的有效性.
- 对PEMS08基准的实验表明了可扩展性和改善了长时间的性能.
- 与香草骨干相比,在120分钟预测中实现了平均绝对误差 (MAE) 的19.1%降低.
结论:
- 该FE-iTransformer框架有效地提高了流量预测的准确性.
- 拟议的FEM和两阶段方法比标准iTransformer模型提供了显著的改进.
- 在图形数据不可用或不可靠时,FE-iTransformer提供了一个强大的,无图形的交通预测解决方案.
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