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Despite this, current prediction models only account for either temporal or spatial features in isolation, without considering their interaction, impeding the model\u2019s 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\u2010of\u2010the\u2010art methods, our proposed algorithm reduces memory consumption and elevates computational efficiency and the practical value. We conduct experiments with real\u2010world datasets, and our proposed model outperformed advanced prediction models.<\/jats:p>","DOI":"10.1155\/2023\/7099652","type":"journal-article","created":{"date-parts":[[2023,12,9]],"date-time":"2023-12-09T20:05:05Z","timestamp":1702152305000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["GDENet: Graph Differential Equation Network for Traffic Flow Prediction"],"prefix":"10.1155","volume":"2023","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-5901-2945","authenticated-orcid":false,"given":"Yanming","family":"Miao","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3961-5649","authenticated-orcid":false,"given":"Xianghong","family":"Tang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6269-0196","authenticated-orcid":false,"given":"Qi","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5212-8546","authenticated-orcid":false,"given":"Liya","family":"Yu","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2023,12,9]]},"reference":[{"key":"e_1_2_11_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2955794"},{"key":"e_1_2_11_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/tkde.2020.3025580"},{"key":"e_1_2_11_3_2","doi-asserted-by":"publisher","DOI":"10.1002\/int.22927"},{"key":"e_1_2_11_4_2","doi-asserted-by":"publisher","DOI":"10.1002\/int.22897"},{"key":"e_1_2_11_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2021.10.021"},{"key":"e_1_2_11_6_2","doi-asserted-by":"publisher","DOI":"10.1002\/int.4550070204"},{"key":"e_1_2_11_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/tits.2019.2900481"},{"key":"e_1_2_11_8_2","doi-asserted-by":"publisher","DOI":"10.1002\/int.22567"},{"key":"e_1_2_11_9_2","article-title":"Lightnet+: a dual-source lightning forecasting network with bi-direction spatiotemporal transformation","volume":"1","author":"Zhou X.","year":"2022","journal-title":"Applied Intelligence"},{"key":"e_1_2_11_10_2","article-title":"Taxi demand forecasting based on the temporal multimodal information fusion graph neural network","volume":"14","author":"Liao W.","year":"2022","journal-title":"Applied Intelligence"},{"key":"e_1_2_11_11_2","doi-asserted-by":"publisher","DOI":"10.1002\/int.22855"},{"key":"e_1_2_11_12_2","doi-asserted-by":"publisher","DOI":"10.1002\/int.20473"},{"key":"e_1_2_11_13_2","doi-asserted-by":"publisher","DOI":"10.1002\/int.23012"},{"key":"e_1_2_11_14_2","doi-asserted-by":"publisher","DOI":"10.1002\/int.22665"},{"key":"e_1_2_11_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107640"},{"key":"e_1_2_11_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2020.01.043"},{"key":"e_1_2_11_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2021.05.035"},{"key":"e_1_2_11_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.09.043"},{"key":"e_1_2_11_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2015.03.014"},{"key":"e_1_2_11_20_2","first-page":"2355","article-title":"Lsgcn: long short-term traffic prediction with graph convolutional networks","volume":"7","author":"Huang R.","year":"2020","journal-title":"IJCAI"},{"key":"e_1_2_11_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/tits.2019.2935152"},{"key":"e_1_2_11_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2020.2978386"},{"key":"e_1_2_11_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/tip.2020.3004249"},{"key":"e_1_2_11_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2023.03.010"},{"key":"e_1_2_11_25_2","doi-asserted-by":"publisher","DOI":"10.3390\/rs14236085"},{"key":"e_1_2_11_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109547"},{"key":"e_1_2_11_27_2","first-page":"6572","article-title":"Neural ordinary differential equations","author":"Chen T. 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