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使用DynamicChebNet基于时间注意力和多图相邻融合的流量预测

Jingbao Zhang^1,2, Junbing Cheng³, Fujia Li¹

¹College of Artificial Intelligence, Taiyuan University of Technology, Taiyuan, 030024, Shanxi, China.

Scientific reports

|October 7, 2025

概括

这项研究引入了一种新的交通流预测模型TMDCN,通过分析时间模式和道路网络结构来提高准确性. 该模型显著减少了城市交通流量的预测错误.

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:

城市交通流量预测对于高效的交通管理至关重要.
现有的模型与复杂的时空相关性和交通数据异质性作斗争.
挑战包括复杂的道路网络和跨时间和空间尺度的变化,导致精度低.

研究的目的:

提出一个新的流量预测模型,TMDCN (使用DynamicChebNet的时间注意力和多图形相邻融合).
为了提高交通流预测的准确性和实时性能.
为了解决捕获交通数据中复杂的时间和空间依赖性的局限性.

主要方法:

开发了一个时间特征提取块,使用注意力机制和多尺度卷积层来捕捉多尺度的时间依赖.
实现了多图形相邻矩阵融合与动态切比舍夫图形卷积网络以建模空间依赖.
综合时间注意力和多图相邻性融合,用于全面的时空分析.

主要成果:

拟议的TMDCN模型在PeMS04和PeMS08数据集上表现出卓越的性能.
在一个小时前的流量预测中,实现了显著的平均绝对误差 (MAE) 降低 (18.33对于PeMS04,13.72对于PeMS08).
超越了传统的交通流量预测方法.

结论:

TMDCN模型有效地捕捉了城市交通中的复杂的时空相关性.
时间注意力和多图融合的整合提高了预测准确性和实时能力.
拟议的方法为智能运输系统提供了一个有前途的解决方案.

关键词:

动态的切布网 (ChebNet) 是一个动态的切布网.多图形相邻矩阵融合.时空空间依赖关系时间注意力时间注意力交通流量预测预测

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