{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T16:37:39Z","timestamp":1782491859022,"version":"3.54.5"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2024,2,10]],"date-time":"2024-02-10T00:00:00Z","timestamp":1707523200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,2,10]],"date-time":"2024-02-10T00:00:00Z","timestamp":1707523200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61772087"],"award-info":[{"award-number":["61772087"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Hunan","award":["2022JJ30621"],"award-info":[{"award-number":["2022JJ30621"]}]},{"name":"Natural Science Foundation of Hunan","award":["2016JJ2005"],"award-info":[{"award-number":["2016JJ2005"]}]},{"name":"Scientific Research Foundation of Hunan Provincial Education Department of China","award":["19B004"],"award-info":[{"award-number":["19B004"]}]},{"name":"Scientific Research Foundation of Hunan Provincial Education Department of China","award":["16B006"],"award-info":[{"award-number":["16B006"]}]},{"name":"Science and Technology Program of Changsha","award":["kq2202215"],"award-info":[{"award-number":["kq2202215"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2024,7]]},"DOI":"10.1007\/s13042-023-02067-2","type":"journal-article","created":{"date-parts":[[2024,2,10]],"date-time":"2024-02-10T03:02:22Z","timestamp":1707534142000},"page":"2827-2841","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["MSSTN: a multi-scale spatio-temporal network for traffic flow prediction"],"prefix":"10.1007","volume":"15","author":[{"given":"Yun","family":"Song","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinke","family":"Bai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wendong","family":"Fan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8518-793X","authenticated-orcid":false,"given":"Zelin","family":"Deng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cong","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,2,10]]},"reference":[{"key":"2067_CR1","doi-asserted-by":"crossref","unstructured":"Park J, Li D, Murphey YL et al (2011) Real time vehicle speed prediction using a neural network traffic model. In: International joint conference on neural networks, pp 2991\u20132996","DOI":"10.1109\/IJCNN.2011.6033614"},{"key":"2067_CR2","unstructured":"Ahmed MS, Cook AR (1979) Analysis of freeway traffic time-series data by using Box\u2013Jenkins techniques. Transportation Research Board, pp 1\u20139"},{"issue":"1","key":"2067_CR3","doi-asserted-by":"publisher","first-page":"132","DOI":"10.3141\/1644-14","volume":"1644","author":"BM Williams","year":"1998","unstructured":"Williams BM, Durvasula PK, Brown DE (1998) Urban freeway traffic flow prediction: application of seasonal autoregressive integrated moving average and exponential smoothing models. Transp Res Rec 1644(1):132\u2013141","journal-title":"Transp Res Rec"},{"issue":"1","key":"2067_CR4","doi-asserted-by":"publisher","first-page":"179","DOI":"10.3141\/1678-22","volume":"1678","author":"S Lee","year":"1999","unstructured":"Lee S, Fambro DB (1999) Application of subset autoregressive integrated moving average model for short-term freeway traffic volume forecasting. Transp Res Rec 1678(1):179\u2013188","journal-title":"Transp Res Rec"},{"issue":"2","key":"2067_CR5","doi-asserted-by":"publisher","first-page":"871","DOI":"10.1109\/TITS.2013.2247040","volume":"14","author":"M Lippi","year":"2013","unstructured":"Lippi M, Bertini M, Frasconi P (2013) Short-term traffic flow forecasting: an experimental comparison of time-series analysis and supervised learning. IEEE Trans Intell Transp Syst 14(2):871\u2013882","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"2067_CR6","doi-asserted-by":"publisher","first-page":"435","DOI":"10.1016\/j.asoc.2014.10.022","volume":"26","author":"R Chen","year":"2015","unstructured":"Chen R, Liang CY, Hong WC et al (2015) Forecasting holiday daily tourist flow based on seasonal support vector regression with adaptive genetic algorithm. Appl Soft Comput 26:435\u2013443","journal-title":"Appl Soft Comput"},{"issue":"2","key":"2067_CR7","first-page":"178","volume":"24","author":"X Zhang","year":"2009","unstructured":"Zhang X, He G, Lu H (2009) Short-term traffic flow forecasting based on K-nearest neighbors non-parametric regression. J Syst Eng 24(2):178\u2013183","journal-title":"J Syst Eng"},{"issue":"8","key":"2067_CR8","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780","journal-title":"Neural Comput"},{"issue":"24","key":"2067_CR9","doi-asserted-by":"publisher","first-page":"17245","DOI":"10.1007\/s00521-021-06315-w","volume":"33","author":"DD Oliveira","year":"2021","unstructured":"Oliveira DD, Rampinelli M, Tozatto GZ et al (2021) Forecasting vehicular traffic flow using MLP and LSTM. Neural Comput Appl 33(24):17245\u201317256","journal-title":"Neural Comput Appl"},{"key":"2067_CR10","doi-asserted-by":"crossref","unstructured":"Zhang J, Zheng Y, Qi D (2017) Deep spatio-temporal residual networks for citywide crowd flows prediction. In: AAAI conference on artificial intelligence (AAAI), vol 31, no 1, pp 1655\u20131661","DOI":"10.1609\/aaai.v31i1.10735"},{"key":"2067_CR11","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S et al (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"2067_CR12","first-page":"802","volume":"28","author":"X Shi","year":"2015","unstructured":"Shi X, Chen Z, Wang H et al (2015) Convolutional LSTM network: a machine learning approach for precipitation nowcasting. Adv Neural Inf Process Syst 28:802\u2013810","journal-title":"Adv Neural Inf Process Syst"},{"key":"2067_CR13","doi-asserted-by":"crossref","unstructured":"Guo S, Lin Y, Feng N et al (2019) Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. In: AAAI conference on artificial intelligence (AAAI), vol 33, no 01, pp 922\u2013929","DOI":"10.1609\/aaai.v33i01.3301922"},{"key":"2067_CR14","doi-asserted-by":"crossref","unstructured":"Yao H, Tang X, Wei H et al (2019) Revisiting spatial-temporal similarity: a deep learning framework for traffic prediction. In: AAAI conference on artificial intelligence (AAAI), vol 33, no 01, pp 5668\u20135675","DOI":"10.1609\/aaai.v33i01.33015668"},{"key":"2067_CR15","unstructured":"Li Y, Yu R, Shahabi C et al (2017) Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv:1707.01926"},{"key":"2067_CR16","unstructured":"Chung J, Gulcehre C, Cho KH et al (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv:1412.3555"},{"key":"2067_CR17","doi-asserted-by":"crossref","unstructured":"Yu B, Yin H, Zhu Z (2017) Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. arXiv:1709.04875","DOI":"10.24963\/ijcai.2018\/505"},{"key":"2067_CR18","doi-asserted-by":"crossref","unstructured":"Geng X, Li Y, Wang L et al (2019) Spatiotemporal multi-graph convolution network for ride-hailing demand forecasting. In: AAAI conference on artificial intelligence(AAAI), vol 33, no 01, pp 3656\u20133663","DOI":"10.1609\/aaai.v33i01.33013656"},{"issue":"9","key":"2067_CR19","doi-asserted-by":"publisher","first-page":"3848","DOI":"10.1109\/TITS.2019.2935152","volume":"21","author":"L Zhao","year":"2019","unstructured":"Zhao L, Song Y, Zhang C et al (2019) T-gcn: a temporal graph convolutional network for traffic prediction. IEEE Trans Intell Transp Syst 21(9):3848\u20133858","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"2067_CR20","unstructured":"Defferrard M, Bresson X, Vandergheynst P (2016) Convolutional neural networks on graphs with fast localized spectral filtering. Adv Neural Inf Process Syst 29"},{"key":"2067_CR21","doi-asserted-by":"crossref","unstructured":"Guo K, Hu Y, Sun Y et al (2021) Hierarchical graph convolution network for traffic forecasting. In: AAAI conference on artificial intelligence (AAAI), vol 35, no 1, pp 151\u2013159","DOI":"10.1609\/aaai.v35i1.16088"},{"key":"2067_CR22","doi-asserted-by":"crossref","unstructured":"Zheng C, Fan X, Wang C et al (2020) Gman: a graph multi-attention network for traffic prediction. In: AAAI conference on artificial intelligence (AAAI), vol 34, no 01, pp 1234\u20131241","DOI":"10.1609\/aaai.v34i01.5477"},{"key":"2067_CR23","first-page":"17804","volume":"33","author":"L Bai","year":"2020","unstructured":"Bai L, Yao L, Li C et al (2020) Adaptive graph convolutional recurrent network for traffic forecasting. Adv Neural Inf Process Syst 33:17804\u201317815","journal-title":"Adv Neural Inf Process Syst"},{"key":"2067_CR24","doi-asserted-by":"crossref","unstructured":"Wu Z, Pan S, Long G et al (2020) Connecting the dots: multivariate time series forecasting with graph neural networks. In: ACM SIGKDD international conference on knowledge discovery & data mining (SIGKDD), vol 26, no 01, pp 753\u2013763","DOI":"10.1145\/3394486.3403118"},{"key":"2067_CR25","doi-asserted-by":"crossref","unstructured":"Choi J, Choi H, Hwang J et al (2022) Graph neural controlled differential equations for traffic forecasting. In: AAAI conference on artificial intelligence (AAAI), vol 36, no 6, pp 6367\u20136374","DOI":"10.1609\/aaai.v36i6.20587"},{"key":"2067_CR26","doi-asserted-by":"crossref","unstructured":"Jiang J, Han C, Zhao WX et al (2023) PDFormer: propagation delay-aware dynamic long-range transformer for traffic flow prediction. arXiv:2301.07945","DOI":"10.1609\/aaai.v37i4.25556"},{"key":"2067_CR27","first-page":"5998","volume":"30","author":"A Vaswani","year":"2017","unstructured":"Vaswani A, Shazeer N, Parmar N et al (2017) Attention is all you need. Adv Neural Inf Process Syst 30:5998\u20136008","journal-title":"Adv Neural Inf Process Syst"},{"key":"2067_CR28","unstructured":"Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. In: International conference on neural information processing systems (NeurIPS), vol 27, no 2, pp 3104\u20133112"},{"key":"2067_CR29","doi-asserted-by":"crossref","unstructured":"Wu Z, Pan S, Long G et al (2019) Graph wavenet for deep spatial-temporal graph modeling. In: International joint conference on artificial intelligence (IJCAI), vol 28, no 01, pp 1907\u20131913","DOI":"10.24963\/ijcai.2019\/264"},{"key":"2067_CR30","doi-asserted-by":"crossref","unstructured":"Li M, Zhu Z (2021) Spatial-temporal fusion graph neural networks for traffic flow forecasting. In: AAAI conference on artificial intelligence (AAAI), vol 35, no 5, pp 4189\u20134196","DOI":"10.1609\/aaai.v35i5.16542"},{"key":"2067_CR31","doi-asserted-by":"crossref","unstructured":"Song C, Lin Y, Guo S et al (2020) Spatial-temporal synchronous graph convolutional networks: a new framework for spatial-temporal network data forecasting. In: AAAI conference on artificial intelligence (AAAI), vol 34, no 1, pp 914\u2013921","DOI":"10.1609\/aaai.v34i01.5438"},{"key":"2067_CR32","unstructured":"Hochreiter S, Bengio Y, Frasconi P et al (2001) Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. Wiley-IEEE Press, pp 237\u2013243"},{"key":"2067_CR33","doi-asserted-by":"crossref","unstructured":"Fang Z, Long Q, Song G et al (2021) Spatial-temporal graph ode networks for traffic flow forecasting. In: ACM SIGKDD conference on knowledge discovery & data mining, vol 27, no 01, pp 364\u2013373","DOI":"10.1145\/3447548.3467430"},{"key":"2067_CR34","unstructured":"Xu M, Dai W, Liu C et al (2020) Spatial-temporal transformer networks for traffic flow forecasting. arXiv:2001.02908"},{"key":"2067_CR35","doi-asserted-by":"crossref","unstructured":"Pan Z, Ke S, Yang X, et al. (2021) AutoSTG: neural architecture search for predictions of spatio-temporal graph. In: Web conference (WWW), vol 21, no 01, pp 1846\u20131855","DOI":"10.1145\/3442381.3449816"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-02067-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-023-02067-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-02067-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,19]],"date-time":"2024-07-19T14:56:54Z","timestamp":1721401014000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-023-02067-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,10]]},"references-count":35,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2024,7]]}},"alternative-id":["2067"],"URL":"https:\/\/doi.org\/10.1007\/s13042-023-02067-2","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,10]]},"assertion":[{"value":"5 November 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 December 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 February 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}