{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T20:47:30Z","timestamp":1768682850105,"version":"3.49.0"},"reference-count":45,"publisher":"Tech Science Press","issue":"2","license":[{"start":{"date-parts":[[2025,2,23]],"date-time":"2025-02-23T00:00:00Z","timestamp":1740268800000},"content-version":"vor","delay-in-days":53,"URL":"https:\/\/doi.org\/10.32604\/TSP-CROSSMARKPOLICY"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["CMC"],"published-print":{"date-parts":[[2025]]},"DOI":"10.32604\/cmc.2024.057494","type":"journal-article","created":{"date-parts":[[2024,12,11]],"date-time":"2024-12-11T21:29:21Z","timestamp":1733952561000},"page":"3517-3537","update-policy":"https:\/\/doi.org\/10.32604\/tsp-crossmarkpolicy","source":"Crossref","is-referenced-by-count":3,"title":["MSSTGCN: Multi-Head Self-Attention and Spatial-Temporal Graph Convolutional Network for Multi-Scale Traffic Flow Prediction"],"prefix":"10.32604","volume":"82","author":[{"given":"Fan","family":"Yu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhen","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xue","family":"Xia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinlu","family":"Zong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"17807","published-online":{"date-parts":[[2025]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","first-page":"718","DOI":"10.1016\/j.ins.2022.06.080","article-title":"TCP-BAST: A novel approach to traffic congestion prediction with bilateral alternation on spatiality and temporality","volume":"608","author":"Zhang","year":"Aug. 2022","journal-title":"Inf. Sci."},{"key":"ref2","doi-asserted-by":"crossref","first-page":"15818","DOI":"10.1109\/TITS.2023.3270749","article-title":"Transportation internet: A sustainable solution for intelligent transportation systems","volume":"24","author":"Li","year":"Dec. 2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref3","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.111946","article-title":"Dynamic spatial aware graph transformer for spatiotemporal traffic flow forecasting","volume":"297","author":"Li","year":"May 2024, Art. no. 111946","journal-title":"Knowl. Based Syst."},{"key":"ref4","article-title":"Gated recurrent graph convolutional attention network for traffic flow prediction","volume":"15","author":"Feng","year":"May 2023, Art. no. 7696","journal-title":"Sustainability"},{"key":"ref5","doi-asserted-by":"crossref","first-page":"8687","DOI":"10.1109\/TITS.2022.3201879","article-title":"A deep learning approach for long-term traffic flow prediction with multifactor fusion using spatiotemporal graph convolutional network","volume":"24","author":"Qi","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref6","doi-asserted-by":"crossref","DOI":"10.1007\/s12544-015-0170-8","article-title":"Short-term traffic flow prediction using seasonal ARIMA model with limited input data","volume":"7","author":"Kumar","year":"Jun. 2015","journal-title":"Eur. Transp. Res. Rev."},{"key":"ref7","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.trc.2015.11.002","article-title":"A spatiotemporal correlative k-nearest neighbor model for short-term traffic multistep forecasting","volume":"62","author":"Cai","year":"2016","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref8","article-title":"Short-term traffic flow prediction of expressway considering spatial influences","volume":"148","author":"Shuai","year":"2022","journal-title":"J. Transp. Eng. A-Syst."},{"key":"ref9","series-title":"2016 31st Youth Acad. Annu. Conf. Chinese Assoc. Autom. (YAC)","first-page":"324","article-title":"Using LSTM and GRU neural network methods for traffic flow prediction","author":"Fu","year":"2016"},{"key":"ref10","series-title":"Proc. 2014 Conf. Empir. Methods Natural Lang. Process.","first-page":"1724","article-title":"Learning phrase representations using RNN rncoder-decoder for statistical machine translation","author":"Cho","year":"2014"},{"key":"ref11","doi-asserted-by":"crossref","DOI":"10.3390\/fi16060189","article-title":"Dynamic spatial-temporal self-attention network for traffic flow prediction","volume":"16","author":"Wang","year":"May 2024","journal-title":"Future Internet"},{"key":"ref12","doi-asserted-by":"crossref","DOI":"10.1016\/j.physa.2023.128913","article-title":"STGC-GNNs: A GNN-based traffic prediction framework with a spatial-temporal granger causality graph","volume":"623","author":"He","year":"2023","journal-title":"Physica A"},{"key":"ref13","doi-asserted-by":"crossref","DOI":"10.3390\/s22228880","article-title":"GLSNN network: A multi-scale spatiotemporal prediction model for urban traffic flow","volume":"22","author":"Cai","year":"2022","journal-title":"Sensors"},{"key":"ref14","doi-asserted-by":"crossref","first-page":"3848","DOI":"10.1109\/TITS.2019.2935152","article-title":"T-GCN: A temporal graph convolutional network for traffic prediction","volume":"21","author":"Zhao","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref15","series-title":"6th Int. Conf. Learn. Rep., ICLR 2018","article-title":"Diffusion convolutional recurrent neural network: Data-driven traffic forecasting","author":"Li","year":"2018"},{"key":"ref16","first-page":"922","article-title":"Attention based spatial-temporal graph convolutional networks for traffic flow forecasting","volume":"33","author":"Guo","year":"Jul. 2019","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref17","doi-asserted-by":"crossref","first-page":"5415","DOI":"10.1109\/TKDE.2021.3056502","article-title":"Learning dynamics and heterogeneity of spatial-temporal graph data for traffic forecasting","volume":"34","author":"Guo","year":"2022","journal-title":"IEEE Trans. Knowl. Data. Eng."},{"key":"ref18","doi-asserted-by":"crossref","first-page":"15457","DOI":"10.1007\/s00521-022-07235-z","article-title":"Forecasting traffic flow with spatial-temporal convolutional graph attention networks","volume":"34","author":"Zhang","year":"Sep. 2022","journal-title":"Neural. Comput. Appl."},{"key":"ref19","doi-asserted-by":"crossref","first-page":"2837","DOI":"10.1109\/TMC.2021.3129796","article-title":"MVSTGN: A multi-view spatial-temporal graph network for cellular traffic prediction","volume":"22","author":"Yao","year":"2023","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref20","first-page":"82","article-title":"A summary of traffic flow forecasting methods","volume":"21","author":"Wei","year":"2004","journal-title":"J. Highw. Transp. Res. Dev."},{"key":"ref21","doi-asserted-by":"crossref","first-page":"1393","DOI":"10.1109\/TITS.2013.2262376","article-title":"Predicting taxi-passenger demand using streaming data","volume":"14","author":"Moreira-Matias","year":"Sep. 2013","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref22","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1142\/S0219477511000405","article-title":"A new traffic speed forecasting method based on bi-pattern recognition","volume":"10","author":"Wang","year":"2011","journal-title":"Fluct. Noise. Lett."},{"key":"ref23","first-page":"1","article-title":"Traffic flow prediction during the holidays based on DFT and SVR","volume":"2019","author":"Luo","year":"2019","journal-title":"J. Sens."},{"key":"ref24","series-title":"2018 World Autom. Congr. (WAC)","first-page":"1","article-title":"On definition of deep learning","author":"Zhang","year":"2018"},{"key":"ref25","doi-asserted-by":"crossref","first-page":"2572","DOI":"10.1109\/TITS.2017.2755684","article-title":"Real-time prediction of taxi demand using recurrent neural networks","volume":"19","author":"Xu","year":"2018","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref26","first-page":"1047","article-title":"Kalman filter-based CNN-BiLSTM-ATT model for traffic flow prediction","volume":"76","author":"Zhang","year":"2023","journal-title":"Comput. Mater. Contin."},{"key":"ref27","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1109\/TITS.2019.2955794","article-title":"TrafficGAN: Network-scale deep traffic prediction with generative adversarial nets","volume":"22","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref28","doi-asserted-by":"crossref","first-page":"522","DOI":"10.1016\/j.ins.2022.08.080","article-title":"Spatial-temporal short-term traffic flow prediction model based on dynamical-learning graph convolution mechanism","volume":"611","author":"Chen","year":"2022","journal-title":"Inf. Sci."},{"key":"ref29","doi-asserted-by":"crossref","first-page":"18293","DOI":"10.1007\/s11227-023-05383-0","article-title":"Spatial-temporal graph convolutional networks for traffic flow prediction considering multiple traffic parameters","volume":"79","author":"Su","year":"Nov. 2023","journal-title":"J. Supercomput."},{"key":"ref30","doi-asserted-by":"crossref","DOI":"10.1016\/j.physa.2021.126736","article-title":"Traffic prediction based on auto spatiotemporal multi-graph adversarial neural network","volume":"590","author":"Wang","year":"2022","journal-title":"Physica A"},{"key":"ref31","doi-asserted-by":"crossref","first-page":"15379","DOI":"10.1109\/TITS.2022.3146899","article-title":"2F-TP: Learning flexible spatiotemporal dependency for flexible traffic prediction","volume":"24","author":"Zhao","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref32","first-page":"15008","article-title":"Traffic flow forecasting with spatial-temporal graph diffusion network","volume":"35","author":"Zhang","year":"May 2021","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref33","series-title":"Proc. 31st Int. Conf. Neural Inf. Process. Syst.","first-page":"6000","article-title":"Attention is all you need","author":"Vaswani","year":"2017"},{"key":"ref34","article-title":"A3T-GCN: Attention temporal graph convolutional network for traffic forecasting","volume":"10","author":"Bai","year":"2021","journal-title":"ISPRS Int. J. Geoinf."},{"key":"ref35","doi-asserted-by":"crossref","DOI":"10.3390\/s23083836","article-title":"Multi-head spatiotemporal attention graph convolutional network for traffic prediction","volume":"23","author":"Oluwasanmi","year":"2023","journal-title":"Sensors"},{"key":"ref36","doi-asserted-by":"crossref","first-page":"47231","DOI":"10.1109\/ACCESS.2024.3381987","article-title":"MTESformer: Multi-scale temporal and enhance spatial transformer for traffic flow prediction","volume":"12","author":"Dong","year":"2024","journal-title":"IEEE Access"},{"key":"ref37","doi-asserted-by":"crossref","DOI":"10.3390\/su16145860","article-title":"Spatiotemporal dynamic multi-hop network for traffic flow forecasting","volume":"16","author":"Chai","year":"2024","journal-title":"Sustainability"},{"key":"ref38","doi-asserted-by":"crossref","first-page":"874","DOI":"10.1109\/TMECH.2011.2161094","article-title":"A socially inspired framework for human state inference using expert opinion integration","volume":"16","author":"Modi","year":"2011","journal-title":"IEEE-ASME T MECH"},{"key":"ref39","series-title":"Proc. 27th Int. Joint Conf. Artif. Intell. (IJCAI)","first-page":"3634","article-title":"Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting","author":"Yu","year":"2018"},{"key":"ref40","first-page":"1234","article-title":"GMAN: A graph multi-attention network for traffic prediction","volume":"34","author":"Zheng","year":"Apr. 2020","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref41","first-page":"9233","article-title":"FC-GAGA: Fully connected gated graph architecture for spatio-temporal traffic forecasting","volume":"35","author":"Oreshkin","year":"2021","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref42","series-title":"Proc. 25th ACM SIGKDD Int. Conf. Knowl. Discov. Data Min. (KDD)","first-page":"1720","article-title":"Urban traffic prediction from spatio-temporal data using deep meta learning","author":"Pan","year":"2019"},{"key":"ref43","series-title":"Proc. Twenty-Eighth Int. Joint Conf. Artif. Intell. (IJCAI)","first-page":"1907","article-title":"Graph WaveNet for deep spatial-temporal graph modeling","author":"Wu","year":"2019"},{"key":"ref44","series-title":"Proc. 26th ACM SIGKDD Int. Conf. Knowl. Discov. Data Min. (KDD)","first-page":"753","article-title":"Connecting the dots: Multivariate time series forecasting with graph neural networks","author":"Wu","year":"2020"},{"key":"ref45","doi-asserted-by":"crossref","DOI":"10.3390\/electronics11193012","article-title":"Traffic flow prediction based on multi-mode spatial-temporal convolution of mixed hop diffuse ODE","volume":"11","author":"Huang","year":"2022","journal-title":"Electronics"}],"container-title":["Computers, Materials &amp; Continua"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/cdn.techscience.cn\/files\/cmc\/2025\/TSP_CMC-82-2\/TSP_CMC_57494\/TSP_CMC_57494.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T06:09:19Z","timestamp":1763100559000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.techscience.com\/cmc\/v82n2\/59449"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":45,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025]]},"published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.32604\/cmc.2024.057494","relation":{},"ISSN":["1546-2226"],"issn-type":[{"value":"1546-2226","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"2024-08-19","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-11-07","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-02-17","order":2,"name":"published","label":"Published Online","group":{"name":"publication_history","label":"Publication History"}}]}}