{"title":"GDENet: Graph Differential Equation Network for Traffic Flow Prediction","authors":"Yanming Miao, Xianghong Tang, Qi Wang, Liya Yu","doi":"10.1155/2023/7099652","DOIUrl":null,"url":null,"abstract":"The accurate prediction of traffic flow is paramount for the advancement of intelligent transportation systems. Despite this, current prediction models only account for either temporal or spatial features in isolation, without considering their interaction, impeding the model’s ability to express itself. In light of this, we propose the graph differential equations network (GDENet), an approach that can effectively mine spatiotemporal correlation. Specifically, we propose a spatiotemporal feature integrator (STFI), which alleviates the error caused by the deviation of the sampling distribution from the overall distribution. By incorporating temporal information into the model for training and combining it with spatial features, we thoroughly explore the spatiotemporal intrinsic association. When compared to state-of-the-art methods, our proposed algorithm reduces memory consumption and elevates computational efficiency and the practical value. We conduct experiments with real-world datasets, and our proposed model outperformed advanced prediction models.","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":5.0000,"publicationDate":"2023-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Intelligent Systems","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1155/2023/7099652","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}
引用次数: 0
Abstract
The accurate prediction of traffic flow is paramount for the advancement of intelligent transportation systems. Despite this, current prediction models only account for either temporal or spatial features in isolation, without considering their interaction, impeding the model’s ability to express itself. In light of this, we propose the graph differential equations network (GDENet), an approach that can effectively mine spatiotemporal correlation. Specifically, we propose a spatiotemporal feature integrator (STFI), which alleviates the error caused by the deviation of the sampling distribution from the overall distribution. By incorporating temporal information into the model for training and combining it with spatial features, we thoroughly explore the spatiotemporal intrinsic association. When compared to state-of-the-art methods, our proposed algorithm reduces memory consumption and elevates computational efficiency and the practical value. We conduct experiments with real-world datasets, and our proposed model outperformed advanced prediction models.
期刊介绍:
The International Journal of Intelligent Systems serves as a forum for individuals interested in tapping into the vast theories based on intelligent systems construction. With its peer-reviewed format, the journal explores several fascinating editorials written by today''s experts in the field. Because new developments are being introduced each day, there''s much to be learned — examination, analysis creation, information retrieval, man–computer interactions, and more. The International Journal of Intelligent Systems uses charts and illustrations to demonstrate these ground-breaking issues, and encourages readers to share their thoughts and experiences.