Peng Liu , Yaodong Zhu , Yang Yang , Jilong Tang , Xiaojiao Jiang , Jinquan Wang
{"title":"用于精确交通流量预测的多尺度频域学习图神经ode","authors":"Peng Liu , Yaodong Zhu , Yang Yang , Jilong Tang , Xiaojiao Jiang , Jinquan Wang","doi":"10.1016/j.neucom.2025.131566","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"658 ","pages":"Article 131566"},"PeriodicalIF":6.5000,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"MSF-GODE: Multi-scale frequency-domain learning in graph neural ODEs for accurate traffic flow forecasting\",\"authors\":\"Peng Liu , Yaodong Zhu , Yang Yang , Jilong Tang , Xiaojiao Jiang , Jinquan Wang\",\"doi\":\"10.1016/j.neucom.2025.131566\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":19268,\"journal\":{\"name\":\"Neurocomputing\",\"volume\":\"658 \",\"pages\":\"Article 131566\"},\"PeriodicalIF\":6.5000,\"publicationDate\":\"2025-09-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Neurocomputing\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0925231225022386\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neurocomputing","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0925231225022386","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
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.
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.