用于精确交通流量预测的多尺度频域学习图神经ode

IF 6.5 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Peng Liu , Yaodong Zhu , Yang Yang , Jilong Tang , Xiaojiao Jiang , Jinquan Wang
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引用次数: 0

摘要

高质量的交通预测对于智能交通系统和智慧城市的发展具有至关重要的作用。然而,交通数据中普遍存在的时空异质性对现有模型在可靠地捕捉复杂和不断变化的交通动态方面提出了重大挑战。此外,经常忽略非热点区域的特征和缺乏有效的跨通道特征融合机制进一步阻碍了预测的准确性和泛化能力。为了解决这些挑战,我们提出了一个新的框架,称为多尺度时空频率感知图常微分方程网络(MSF-GODE),它提供了一个统一和系统的建模策略来解决上述限制。具体来说,该模型首先利用了一个多尺度频率样本发生器,该发生器利用时频分解来提取周期结构并捕获跨多个分辨率的时间依赖性。然后,它结合了一个时空特征提取器,将关键特征选择和对比学习相结合,从而增强了模型表示非关键区域的能力。最后,利用时空频域特征融合模块对结构演化进行建模,更有效地整合多通道特征。在六个真实交通数据集上进行的大量实验表明,MSF-GODE在预测精度和泛化方面明显优于现有的最先进的方法,为异构环境中的交通预测提供了一个强大而有效的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MSF-GODE: Multi-scale frequency-domain learning in graph neural ODEs for accurate traffic flow forecasting
High-quality traffic forecasting plays a critical role in intelligent transportation systems (ITS) and the development of smart cities. However, the pervasive spatiotemporal heterogeneity in traffic data poses significant challenges for existing models in reliably capturing complex and evolving traffic dynamics. In addition, the frequent neglect of features from non-hotspot regions and the absence of effective cross-channel feature fusion mechanisms further hinder both predictive accuracy and generalization capabilities. To address these challenges, we propose a novel framework named Multi-Scale Spatiotemporal Frequency-aware Graph Ordinary Differential Equation network (MSF-GODE), which offers a unified and systematic modeling strategy to tackle the above limitations. Specifically, the model first utilizes a multi-scale frequency sample generator that leverages time–frequency decomposition to extract periodic structures and capture temporal dependencies across multiple resolutions. It then incorporates a spatiotemporal feature extractor that combines key feature selection and contrastive learning, thereby enhancing the model’s ability to represent non-key regions. Finally, a spatiotemporal frequency-domain feature fusion module is employed to model structural evolution and integrate multi-channel features more effectively. Extensive experiments conducted on six real-world traffic datasets demonstrate that MSF-GODE significantly outperforms existing state-of-the-art methods in terms of both prediction accuracy and generalization, offering a robust and effective solution for traffic forecasting in heterogeneous environments.
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来源期刊
Neurocomputing
Neurocomputing 工程技术-计算机:人工智能
CiteScore
13.10
自引率
10.00%
发文量
1382
审稿时长
70 days
期刊介绍: Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.
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