{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T04:38:40Z","timestamp":1777696720387,"version":"3.51.4"},"reference-count":45,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Intelligent Data Analysis: An International Journal"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:p>Traffic flow prediction can improve transportation efficiency, which is an important part of intelligent transportation systems. In recent years, the prediction method based on graph convolutional recurrent neural network has been widely used in traffic flow prediction. However, in real application scenarios, the spatial dependence of graph signals will change with time, and the filter using a fixed graph displacement operator cannot accurately predict traffic flow at the current moment. To improve the accuracy of traffic flow prediction, a two-layer graph convolutional recurrent neural network based on the dynamic graph displacement operator is proposed. The framework of our proposal is to use the first layer of static graph convolutional recurrent neural network to generate the sequence wave vector of the graph displacement operator. The sequence wave vector is passed through the deconvolutional neural network to obtain the sequence dynamic graph displacement operator, and then the second layer dynamic graph convolutional recurrent neural network is used to predict the traffic flow at the next moment. The model is evaluated on the METR-LA and PEMS-BAY datasets. Experimental results demonstrate that our model significantly outperforms other baseline models.<\/jats:p>","DOI":"10.3233\/ida-230174","type":"journal-article","created":{"date-parts":[[2024,6,14]],"date-time":"2024-06-14T11:08:30Z","timestamp":1718363310000},"page":"59-73","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Two-layer dynamic graph convolutional recurrent neural network for traffic flow prediction"],"prefix":"10.1177","volume":"29","author":[{"given":"Xiangyi","family":"Lu","sequence":"first","affiliation":[{"name":"The School of Computer Science, Shaanxi Normal University, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feng","family":"Tian","sequence":"additional","affiliation":[{"name":"The School of Computer Science, Shaanxi Normal University, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yumeng","family":"Shen","sequence":"additional","affiliation":[{"name":"The School of Computer Science, Shaanxi Normal University, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuejun","family":"Zhang","sequence":"additional","affiliation":[{"name":"The School of Electronics and Information Engineering, Lanzhou Jiaotong University, Gansu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2025,3,28]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1080\/10248079508903828"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-22363-6_7"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/2996913.2996974"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/jiot.2019.2902815"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-017-2492-z"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1061\/(asce)0733-947x(2003)129:6(664)"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2015.11.002"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2014.10.022"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","unstructured":"Wu Y. Tan H. Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework arXiv 2016. doi: 10.48550\/ARXIV.1612.01022.","DOI":"10.48550\/ARXIV.1612.01022"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","unstructured":"Chung J. Gulcehre C. Cho K. Bengio Y. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling arXiv 2014. doi: 10.48550\/ARXIV.1412.3555.","DOI":"10.48550\/ARXIV.1412.3555"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.3390\/s17040818"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1145\/2996913.2997016"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10735"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2015.03.014"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","unstructured":"Cui Z. Ke R. Pu Z. Wang Y. Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction arXiv 2018. doi: 10.48550\/ARXIV.1801.02143.","DOI":"10.48550\/ARXIV.1801.02143"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611974973.87"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.3390\/s17071501"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/icdm.2018.00107"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/tits.2019.2906365"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/tits.2020.2997352"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","unstructured":"Kipf T.N. Welling M. Semi-Supervised Classification with Graph Convolutional Networks arXiv 2016. doi: 10.48550\/ARXIV.1609.02907.","DOI":"10.48550\/ARXIV.1609.02907"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","unstructured":"Henaff M. Bruna J. LeCun Y. Deep Convolutional Networks on Graph-Structured Data arXiv 2015. doi: 10.48550\/ARXIV.1506.05163.","DOI":"10.48550\/ARXIV.1506.05163"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/505"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1109\/tits.2019.2935152"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","unstructured":"Li Y. Yu R. Shahabi C. Liu Y. Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting arXiv 2017. doi: 10.48550\/ARXIV.1707.01926. ???","DOI":"10.48550\/ARXIV.1707.01926"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2017.11"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","unstructured":"Veli\u010dkovi\u0107 P. Cucurull G. Casanova A. Romero A. Li\u00f2 P. Bengio Y. Graph Attention Networks arXiv 2017. doi: 10.48550\/ARXIV.1710.10903.","DOI":"10.48550\/ARXIV.1710.10903"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1109\/jiot.2020.2974494"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301922"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5477"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","unstructured":"Zhou Z. Li X. Graph Convolution: A High-Order and Adaptive Approach arXiv 2017. doi: 10.48550\/ARXIV.1706.09916.","DOI":"10.48550\/ARXIV.1706.09916"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/tits.2019.2950416"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","unstructured":"Wu Z. Pan S. Long G. Jiang J. Zhang C. Graph WaveNet for Deep Spatial-Temporal Graph Modeling (2019). doi: 10.48550\/ARXIV.1906.00121.","DOI":"10.48550\/ARXIV.1906.00121"},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403118"},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","unstructured":"Abu-El-Haija S. Perozzi B. Kapoor A. Alipourfard N. Lerman K. Harutyunyan H. Steeg G.V. Galstyan A. MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing arXiv 2019. doi: 10.48550\/ARXIV.1905.00067.","DOI":"10.48550\/ARXIV.1905.00067"},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301890"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","unstructured":"Yang S. Liu J. Zhao K. Space Meets Time: Local Spacetime Neural Network For Traffic Flow Forecasting arXiv 2021. doi: 10.48550\/ARXIV.2109.05225.","DOI":"10.48550\/ARXIV.2109.05225"},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1109\/tits.2021.3072743"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1109\/tits.2022.3219626"},{"key":"e_1_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.1109\/tits.2022.3220915"},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330884"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","unstructured":"Dai H. Li H. Tian T. Huang X. Wang L. Zhu J. Song L. Adversarial Attack on Graph Structured Data arXiv 2018. doi: 10.48550\/ARXIV.1806.02371.","DOI":"10.48550\/ARXIV.1806.02371"},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220078"},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330905"}],"container-title":["Intelligent Data Analysis: An International Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/IDA-230174","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/IDA-230174","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/IDA-230174","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:20:46Z","timestamp":1777454446000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/IDA-230174"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1]]},"references-count":45,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["10.3233\/IDA-230174"],"URL":"https:\/\/doi.org\/10.3233\/ida-230174","relation":{},"ISSN":["1088-467X","1571-4128"],"issn-type":[{"value":"1088-467X","type":"print"},{"value":"1571-4128","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1]]}}}