{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T00:45:20Z","timestamp":1774313120043,"version":"3.50.1"},"reference-count":28,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T00:00:00Z","timestamp":1658102400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T00:00:00Z","timestamp":1658102400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,7,18]]},"DOI":"10.1109\/ijcnn55064.2022.9892326","type":"proceedings-article","created":{"date-parts":[[2022,9,30]],"date-time":"2022-09-30T19:56:04Z","timestamp":1664567764000},"page":"1-8","source":"Crossref","is-referenced-by-count":35,"title":["Adaptive Spatial-Temporal Fusion Graph Convolutional Networks for Traffic Flow Forecasting"],"prefix":"10.1109","author":[{"given":"Senwen","family":"Li","sequence":"first","affiliation":[{"name":"College of Computer Science, Chongqing University,China"}]},{"given":"Liang","family":"Ge","sequence":"additional","affiliation":[{"name":"College of Computer Science, Chongqing University,China"}]},{"given":"Yongquan","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Computer Science, Chongqing University,China"}]},{"given":"Bo","family":"Zeng","sequence":"additional","affiliation":[{"name":"College of Computer Science, Chongqing University,China"}]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)0733-947X(2003)129:6(664)"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.3390\/app8020277"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2014.02.009"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2013.2247040"},{"key":"ref14","article-title":"Convolutional lstm network: a machine learning approach for precipitation nowcasting","author":"shi","year":"2015","journal-title":"Advances in neural information processing systems"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10735"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2906365"},{"key":"ref17","article-title":"Convolutional neural networks on graphs with fast localized spectral filtering","author":"defferrard","year":"2016","journal-title":"Advances in neural information processing systems"},{"key":"ref18","article-title":"Semi-supervised classification with graph convolutional networks","author":"kipf","year":"0","journal-title":"International Conference on Learning Representations"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2935152"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.3141\/1748-12"},{"key":"ref4","article-title":"Diffusion convolutional recurrent neural network: data-driven traffic forecasting","author":"li","year":"0","journal-title":"International Conference on Learning Representations"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16542"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/4145353"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5438"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/505"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN52387.2021.9533319"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/274"},{"key":"ref2","first-page":"155","article-title":"Support vector regression machines","volume":"9","author":"drucker","year":"1996","journal-title":"Advances in neural information processing systems"},{"key":"ref9","first-page":"82","article-title":"A summary of traffic flow forecasting methods","volume":"3","author":"liu","year":"2004","journal-title":"Journal of highway and transportation research and development"},{"key":"ref1","first-page":"385","article-title":"Vector autoregressive models for multivariate time series","author":"zivot","year":"2006","journal-title":"Modeling Financial Time Series with S-PLUS"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330884"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/264"},{"key":"ref21","article-title":"Graph attention networks","author":"veli?kovi?","year":"0","journal-title":"International Conference on Learning Representations"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301922"},{"key":"ref23","article-title":"Wavenet: a generative model for raw audio","author":"oord","year":"0","journal-title":"The 9th Speech Synthesis Workshop"},{"key":"ref26","article-title":"Adaptive graph convolutional recurrent network for traffic forecasting","author":"bai","year":"2020","journal-title":"Advances in neural information processing systems"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5477"}],"event":{"name":"2022 International Joint Conference on Neural Networks (IJCNN)","location":"Padua, Italy","start":{"date-parts":[[2022,7,18]]},"end":{"date-parts":[[2022,7,23]]}},"container-title":["2022 International Joint Conference on Neural Networks (IJCNN)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9891857\/9889787\/09892326.pdf?arnumber=9892326","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T22:56:34Z","timestamp":1667516194000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9892326\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,18]]},"references-count":28,"URL":"https:\/\/doi.org\/10.1109\/ijcnn55064.2022.9892326","relation":{},"subject":[],"published":{"date-parts":[[2022,7,18]]}}}