{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T05:02:35Z","timestamp":1773291755584,"version":"3.50.1"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"16","license":[{"start":{"date-parts":[[2023,5,13]],"date-time":"2023-05-13T00:00:00Z","timestamp":1683936000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,5,13]],"date-time":"2023-05-13T00:00:00Z","timestamp":1683936000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100013058","name":"Jiangsu Provincial Key Research and Development Program","doi-asserted-by":"publisher","award":["BE20187544"],"award-info":[{"award-number":["BE20187544"]}],"id":[{"id":"10.13039\/501100013058","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52272344"],"award-info":[{"award-number":["52272344"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"published-print":{"date-parts":[[2023,11]]},"DOI":"10.1007\/s11227-023-05383-0","type":"journal-article","created":{"date-parts":[[2023,5,15]],"date-time":"2023-05-15T11:58:17Z","timestamp":1684151897000},"page":"18293-18312","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Spatial-temporal graph convolutional networks for traffic flow prediction considering multiple traffic parameters"],"prefix":"10.1007","volume":"79","author":[{"given":"Ziyi","family":"Su","sequence":"first","affiliation":[]},{"given":"Tong","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Xiatong","family":"Hao","sequence":"additional","affiliation":[]},{"given":"Xiaojian","family":"Hu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,5,13]]},"reference":[{"key":"5383_CR1","doi-asserted-by":"publisher","first-page":"1847","DOI":"10.1109\/TITS.2018.2843349","volume":"20","author":"J Mackenzie","year":"2019","unstructured":"Mackenzie J, Roddick JF, Zito R (2019) An evaluation of HTM and LSTM for short-term arterial traffic flow prediction. IEEE Trans Intell Transp Syst 20:1847\u20131857. https:\/\/doi.org\/10.1109\/TITS.2018.2843349","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"5383_CR2","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.trc.2014.01.005","volume":"43","author":"EI Vlahogianni","year":"2014","unstructured":"Vlahogianni EI, Karlaftis MG, Golias JC (2014) Short-term traffic forecasting: where we are and where we\u2019re going. Transp Res Part C Emerg Technol 43:3\u201319. https:\/\/doi.org\/10.1016\/j.trc.2014.01.005","journal-title":"Transp Res Part C Emerg Technol"},{"key":"5383_CR3","doi-asserted-by":"publisher","first-page":"664","DOI":"10.1061\/(ASCE)0733-947X(2003)129:6(664)","volume":"129","author":"MW Billy","year":"2003","unstructured":"Billy MW, Lester AH (2003) Modeling and forecasting vehicular traffic flow as a seasonal arima process: theoretical basis and empirical results. J Transp Eng 129:664\u2013672","journal-title":"J Transp Eng"},{"key":"5383_CR4","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1016\/j.trc.2016.10.019","volume":"73","author":"F Gaetano","year":"2016","unstructured":"Gaetano F, Chiara C, Natalia I (2016) Short-term speed predictions exploiting big data on large urban road networks. Transp Res Part C 73:183\u2013201","journal-title":"Transp Res Part C"},{"key":"5383_CR5","doi-asserted-by":"crossref","unstructured":"Ojeda LL, Kibangou AY, Wit C (2013) Adaptive Kalman filtering for multi-step ahead traffic flow prediction. In: American control conference (ACC), 2013","DOI":"10.1109\/ACC.2013.6580568"},{"key":"5383_CR6","doi-asserted-by":"publisher","first-page":"120642","DOI":"10.1016\/j.physa.2019.03.007","volume":"534","author":"J Tang","year":"2019","unstructured":"Tang J, Chen X, Hu Z et al (2019) Traffic flow prediction based on combination of support vector machine and data denoising schemes. Phys AStatist Mech Appl 534:120642. https:\/\/doi.org\/10.1016\/j.physa.2019.03.007","journal-title":"Phys AStatist Mech Appl"},{"key":"5383_CR7","doi-asserted-by":"publisher","first-page":"91","DOI":"10.3141\/2432-11","volume":"2432","author":"J Sun","year":"2014","unstructured":"Sun J, Sun J, Chen P (2014) Use of support vector machine models for real-time prediction of crash risk on urban expressways. Transp Res Rec 2432:91\u201398. https:\/\/doi.org\/10.3141\/2432-11","journal-title":"Transp Res Rec"},{"key":"5383_CR8","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1016\/j.trc.2012.08.004","volume":"27","author":"J Wang","year":"2013","unstructured":"Wang J, Shi Q (2013) Short-term traffic speed forecasting hybrid model based on Chaos-Wavelet analysis-support vector machine theory. Transp Res Part C Emerg Technol 27:219\u2013232. https:\/\/doi.org\/10.1016\/j.trc.2012.08.004","journal-title":"Transp Res Part C Emerg Technol"},{"key":"5383_CR9","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1007\/s11116-012-9443-4","volume":"40","author":"E Castillo","year":"2013","unstructured":"Castillo E, Jim\u00e9nez P, Men\u00e9ndez JM, Nogal M (2013) A Bayesian method for estimating traffic flows based on plate scanning. Transportation 40:173\u2013201. https:\/\/doi.org\/10.1007\/s11116-012-9443-4","journal-title":"Transportation"},{"key":"5383_CR10","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/j.trc.2015.11.002","volume":"62","author":"P Cai","year":"2016","unstructured":"Cai P, Wang Y, Lu G et al (2016) A spatiotemporal correlative k-nearest neighbor model for short-term traffic multistep forecasting. Transp Res Part C Emerg Technol 62:21\u201334. https:\/\/doi.org\/10.1016\/j.trc.2015.11.002","journal-title":"Transp Res Part C Emerg Technol"},{"key":"5383_CR11","doi-asserted-by":"publisher","first-page":"04016018","DOI":"10.1061\/(ASCE)TE.1943-5436.0000816","volume":"142","author":"B Yu","year":"2016","unstructured":"Yu B, Song X, Guan F et al (2016) k-nearest neighbor model for multiple-time-step prediction of short-term traffic condition. J Transp Eng 142:04016018. https:\/\/doi.org\/10.1061\/(ASCE)TE.1943-5436.0000816","journal-title":"J Transp Eng"},{"key":"5383_CR12","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1016\/j.trc.2014.02.009","volume":"43","author":"Z Zheng","year":"2014","unstructured":"Zheng Z, Su D (2014) Short-term traffic volume forecasting: a k-nearest neighbor approach enhanced by constrained linearly sewing principle component algorithm. Transp Res Part C: Emerg Technol 43:143\u2013157. https:\/\/doi.org\/10.1016\/j.trc.2014.02.009","journal-title":"Transp Res Part C: Emerg Technol"},{"key":"5383_CR13","doi-asserted-by":"publisher","first-page":"818","DOI":"10.3390\/s17040818","volume":"17","author":"X Ma","year":"2017","unstructured":"Ma X, Dai Z, He Z et al (2017) Learning traffic as images: a deep convolutional neural network for large-scale transportation network speed prediction. Sensors 17:818. https:\/\/doi.org\/10.3390\/s17040818","journal-title":"Sensors"},{"key":"5383_CR14","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/j.trc.2012.04.007","volume":"31","author":"F Zheng","year":"2013","unstructured":"Zheng F, Van Zuylen H (2013) Urban link travel time estimation based on sparse probe vehicle data. Trans Res Part C Emerg Technol 31:145\u2013157. https:\/\/doi.org\/10.1016\/j.trc.2012.04.007","journal-title":"Trans Res Part C Emerg Technol"},{"key":"5383_CR15","doi-asserted-by":"crossref","unstructured":"Ye Q, Wong SC, Szeto WY (2010) Short-term traffic speed forecasting based on data recorded at irregular intervals. Transp Urban Sustain 601\u2013601","DOI":"10.1109\/ITSC.2010.5625184"},{"key":"5383_CR16","doi-asserted-by":"publisher","first-page":"4798","DOI":"10.1109\/TITS.2019.2947145","volume":"21","author":"Y Liu","year":"2020","unstructured":"Liu Y, Liu Z, Lyu C, Ye J (2020) Attention-based deep ensemble net for large-scale online taxi-hailing demand prediction. IEEE Trans Intell Transp Syst 21:4798\u20134807. https:\/\/doi.org\/10.1109\/TITS.2019.2947145","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"5383_CR17","doi-asserted-by":"publisher","first-page":"6910","DOI":"10.1109\/TITS.2020.2997352","volume":"22","author":"H Zheng","year":"2021","unstructured":"Zheng H, Lin F, Feng X, Chen Y (2021) A hybrid deep learning model with attention-based conv-LSTM networks for short-term traffic flow prediction. IEEE Trans Intell Transp Syst 22:6910\u20136920. https:\/\/doi.org\/10.1109\/TITS.2020.2997352","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"5383_CR18","doi-asserted-by":"publisher","first-page":"877","DOI":"10.1111\/mice.12450","volume":"34","author":"Y Zhang","year":"2019","unstructured":"Zhang Y, Cheng T, Ren Y (2019) A graph deep learning method for short-term traffic forecasting on large road networks. Comput Aided Civil Infrastruct Eng 34:877\u2013896. https:\/\/doi.org\/10.1111\/mice.12450","journal-title":"Comput Aided Civil Infrastruct Eng"},{"key":"5383_CR19","unstructured":"Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. arXiv:160902907 [cs, stat]"},{"key":"5383_CR20","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.trc.2020.01.010","volume":"112","author":"T Bogaerts","year":"2020","unstructured":"Bogaerts T, Masegosa AD, Angarita-Zapata JS et al (2020) A graph CNN-LSTM neural network for short and long-term traffic forecasting based on trajectory data. Trans Res Part C Emer Technol 112:62\u201377. https:\/\/doi.org\/10.1016\/j.trc.2020.01.010","journal-title":"Trans Res Part C Emer Technol"},{"key":"5383_CR21","doi-asserted-by":"publisher","first-page":"63349","DOI":"10.1109\/ACCESS.2019.2915364","volume":"8","author":"Z Xie","year":"2020","unstructured":"Xie Z, Lv W, Huang S et al (2020) Sequential graph neural network for urban road traffic speed prediction. IEEE Access 8:63349\u201363358. https:\/\/doi.org\/10.1109\/ACCESS.2019.2915364","journal-title":"IEEE Access"},{"key":"5383_CR22","doi-asserted-by":"publisher","first-page":"5549","DOI":"10.1007\/s00521-021-06708-x","volume":"34","author":"X Zhou","year":"2022","unstructured":"Zhou X, Zhang Y, Li Z et al (2022) Large-scale cellular traffic prediction based on graph convolutional networks with transfer learning. Neural Comput Appl 34:5549\u20135559. https:\/\/doi.org\/10.1007\/s00521-021-06708-x","journal-title":"Neural Comput Appl"},{"key":"5383_CR23","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1016\/j.physa.2003.10.017","volume":"333","author":"BS Kerner","year":"2004","unstructured":"Kerner BS (2004) Three-phase traffic theory and highway capacity. Physica A 333:379\u2013440. https:\/\/doi.org\/10.1016\/j.physa.2003.10.017","journal-title":"Physica A"},{"key":"5383_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2019.123725","author":"X Hu","year":"2019","unstructured":"Hu X, Hao X, Wang H et al (2019) Research on on-street temporary parking effects based on cellular automaton model under the framework of Kerner\u2019s three-phase traffic theory. Phys A Stat Mech Appl. https:\/\/doi.org\/10.1016\/j.physa.2019.123725","journal-title":"Phys A Stat Mech Appl"},{"key":"5383_CR25","doi-asserted-by":"publisher","first-page":"85","DOI":"10.2991\/ijcis.d.200120.001","volume":"13","author":"S Du","year":"2020","unstructured":"Du S, Li T, Gong X, Horng S-J (2020) A hybrid method for traffic flow forecasting using multimodal deep learning. IJCIS 13:85. https:\/\/doi.org\/10.2991\/ijcis.d.200120.001","journal-title":"IJCIS"},{"key":"5383_CR26","doi-asserted-by":"publisher","first-page":"125495","DOI":"10.1016\/j.physa.2020.125495","volume":"563","author":"X Hu","year":"2021","unstructured":"Hu X, Liu T, Hao X et al (2021) Research on the influence of bus bay on traffic flow in adjacent lane: simulations in the framework of Kerner\u2019s three-phase traffic theory. Phys A Stat Mech Appl 563:125495. https:\/\/doi.org\/10.1016\/j.physa.2020.125495","journal-title":"Phys A Stat Mech Appl"},{"key":"5383_CR27","doi-asserted-by":"crossref","unstructured":"Han L, Du B, Sun L, et al (2021) Dynamic and Multi-faceted spatio-temporal deep learning for traffic speed forecasting. In: Proceedings of the 27th ACM SIGKDD conference on Knowledge Discovery & Data mining. ACM, Virtual Event Singapore, pp 547\u2013555","DOI":"10.1145\/3447548.3467275"},{"key":"5383_CR28","doi-asserted-by":"publisher","first-page":"12686","DOI":"10.1007\/s11227-022-04386-7","volume":"78","author":"X Hu","year":"2022","unstructured":"Hu X, Liu T, Hao X, Lin C (2022) Attention-based Conv-LSTM and Bi-LSTM networks for large-scale traffic speed prediction. J Supercomput 78:12686\u201312709. https:\/\/doi.org\/10.1007\/s11227-022-04386-7","journal-title":"J Supercomput"},{"key":"5383_CR29","unstructured":"Shi X, Chen Z, Wang H, et al (2015) Convolutional LSTM network: a machine learning approach for precipitation nowcasting"},{"key":"5383_CR30","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1016\/j.trc.2015.03.014","volume":"54","author":"X Ma","year":"2015","unstructured":"Ma X, Tao Z, Wang Y et al (2015) Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transp Res Part C Emerg Technol 54:187\u2013197. https:\/\/doi.org\/10.1016\/j.trc.2015.03.014","journal-title":"Transp Res Part C Emerg Technol"},{"key":"5383_CR31","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1016\/j.procs.2014.05.435","volume":"32","author":"P Brian","year":"2014","unstructured":"Brian P, Mandar K (2014) Adaptive traffic speed estimation. Procedia Comp Sci 32:356\u2013363","journal-title":"Procedia Comp Sci"},{"key":"5383_CR32","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1177\/0361198119841291","volume":"2673","author":"MK Hosseini","year":"2019","unstructured":"Hosseini MK, Talebpour A (2019) Traffic prediction using time-space diagram: a convolutional neural network approach. Transp Res Rec: J Transp Res Board 2673:425\u2013435","journal-title":"Transp Res Rec: J Transp Res Board"},{"key":"5383_CR33","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2014.2345663","author":"Y Lv","year":"2019","unstructured":"Lv Y, Duan Y, Kang W et al (2019) Traffic flow prediction with big data: a deep learning approach. IEEE Trans Int Transp Syst. https:\/\/doi.org\/10.1109\/TITS.2014.2345663","journal-title":"IEEE Trans Int Transp Syst"},{"key":"5383_CR34","doi-asserted-by":"publisher","first-page":"036119812091105","DOI":"10.1177\/0361198120911052","volume":"2674","author":"R Ke","year":"2020","unstructured":"Ke R, Li W, Cui Z, Wang Y (2020) Two-stream multi-channel convolutional neural network for multi-lane traffic speed prediction considering traffic volume impact. Transp Res Rec J Transp Res Board 2674:036119812091105","journal-title":"Transp Res Rec J Transp Res Board"},{"issue":"23","key":"5383_CR35","doi-asserted-by":"publisher","first-page":"5277","DOI":"10.3390\/s19235277","volume":"19","author":"H Tampubolon","year":"2019","unstructured":"Tampubolon H, Yang CL, Chan AS et al (2019) Optimized capsnet for traffic jam speed prediction using mobile sensor data under urban swarming transportation. Sensors 19(23):5277. https:\/\/doi.org\/10.3390\/s19235277","journal-title":"Sensors"},{"key":"5383_CR36","doi-asserted-by":"publisher","first-page":"2036","DOI":"10.1002\/int.22370","volume":"36","author":"Y Xiao","year":"2021","unstructured":"Xiao Y, Yin H, Zhang Y et al (2021) A dual-stage attention-based Conv-LSTM network for spatio-temporal correlation and multivariate time series prediction. Int J Intell Syst 36:2036\u20132057. https:\/\/doi.org\/10.1002\/int.22370","journal-title":"Int J Intell Syst"},{"key":"5383_CR37","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1177\/0361198120947421","volume":"2674","author":"A Shabarek","year":"2020","unstructured":"Shabarek A, Chien S, Hadri S (2020) Deep learning framework for freeway speed prediction in adverse weather. Transp Res Rec 2674:28\u201341. https:\/\/doi.org\/10.1177\/0361198120947421","journal-title":"Transp Res Rec"},{"key":"5383_CR38","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1177\/0361198118776139","volume":"2672","author":"Y Hou","year":"2018","unstructured":"Hou Y, Edara P (2018) Network scale travel time prediction using deep learning. Transp Res Rec 2672:115\u2013123. https:\/\/doi.org\/10.1177\/0361198118776139","journal-title":"Transp Res Rec"},{"key":"5383_CR39","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-022-04386-7","author":"X Hu","year":"2022","unstructured":"Hu X, Liu T, Hao X, Lin C (2022) Attention-based Conv-LSTM and Bi-LSTM networks for large-scale traffic speed prediction. J Supercomput. https:\/\/doi.org\/10.1007\/s11227-022-04386-7","journal-title":"J Supercomput"},{"key":"5383_CR40","doi-asserted-by":"crossref","unstructured":"Azad R, Asadi-Aghbolaghi M, Fathy M, Escalera S (2019) Bi-directional ConvLSTM U-Net with densley connected convolutions","DOI":"10.1109\/ICCVW.2019.00052"},{"key":"5383_CR41","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1016\/j.trc.2018.03.001","volume":"90","author":"Y Wu","year":"2018","unstructured":"Wu Y, Tan H, Qin L et al (2018) A hybrid deep learning based traffic flow prediction method and its understanding. Transp Res Part C Emerg Technol 90:166\u2013180. https:\/\/doi.org\/10.1016\/j.trc.2018.03.001","journal-title":"Transp Res Part C Emerg Technol"},{"key":"5383_CR42","doi-asserted-by":"crossref","unstructured":"Lu B, Gan X, Jin H, et al (2020) spatiotemporal adaptive gated graph convolution network for urban traffic flow forecasting. In: Proceedings of the 29th ACM international conference on Information & Knowledge Management. ACM, Virtual Event Ireland, pp 1025\u20131034","DOI":"10.1145\/3340531.3411894"},{"key":"5383_CR43","doi-asserted-by":"crossref","unstructured":"Golovnin O, Perevozchikov N (2021) E-STGCN: enhanced spatial-temporal graph convolutional network for road traffic forecasting. In: 2021 international conference on Information Technology and Nanotechnology (ITNT). IEEE, Samara, Russian Federation, pp 1\u20134","DOI":"10.1109\/ITNT52450.2021.9649044"},{"key":"5383_CR44","doi-asserted-by":"publisher","first-page":"3848","DOI":"10.1109\/TITS.2019.2935152","volume":"21","author":"L Zhao","year":"2020","unstructured":"Zhao L, Song Y, Zhang C et al (2020) T-GCN: a temporal graph convolutionalnetwork for traffic prediction. IEEE Trans Intell Transp Syst 21:3848\u20133858. https:\/\/doi.org\/10.1109\/TITS.2019.2935152","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"5383_CR45","doi-asserted-by":"publisher","first-page":"922","DOI":"10.1609\/aaai.v33i01.3301922","volume":"33","author":"S Guo","year":"2019","unstructured":"Guo S, Lin Y, Feng N et al (2019) Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. AAAI 33:922\u2013929. https:\/\/doi.org\/10.1609\/aaai.v33i01.3301922","journal-title":"AAAI"},{"key":"5383_CR46","doi-asserted-by":"publisher","first-page":"4883","DOI":"10.1109\/TITS.2019.2950416","volume":"21","author":"Z Cui","year":"2020","unstructured":"Cui Z, Henrickson K, Ke R, Wang Y (2020) Traffic graph convolutional recurrent neural network: a deep learning framework for network-scale traffic learning and forecasting. IEEE Trans Intell Transp Syst 21:4883\u20134894. https:\/\/doi.org\/10.1109\/TITS.2019.2950416","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"5383_CR47","doi-asserted-by":"publisher","first-page":"6402","DOI":"10.3390\/s21196402","volume":"21","author":"D Liu","year":"2021","unstructured":"Liu D, Xu X, Xu W, Zhu B (2021) Graph convolutional network: traffic speed prediction fused with traffic flow data. Sensors 21:6402. https:\/\/doi.org\/10.3390\/s21196402","journal-title":"Sensors"},{"key":"5383_CR48","doi-asserted-by":"crossref","unstructured":"Zhu J, Tao C, Deng H, et al (2020) AST-GCN: attribute-augmented spatiotemporal graph convolutional network for traffic forecasting. arXiv:201111004 [cs]","DOI":"10.1109\/ACCESS.2021.3062114"},{"key":"5383_CR49","doi-asserted-by":"publisher","first-page":"96","DOI":"10.3141\/1748-12","volume":"1748","author":"C Chen","year":"2001","unstructured":"Chen C, Petty K, Skabardonis A et al (2001) Freeway performance measurement system: mining loop detector data. Transp Res Rec 1748:96\u2013102. https:\/\/doi.org\/10.3141\/1748-12","journal-title":"Transp Res Rec"},{"key":"5383_CR50","doi-asserted-by":"crossref","unstructured":"Huang R, Huang C, Liu Y, et al (2021) LSGCN: long short-term traffic prediction with graph convolutional networks. In: Proceedings of the twenty-ninth international joint conference on Artificial Intelligence","DOI":"10.24963\/ijcai.2020\/326"},{"key":"5383_CR51","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/j.neucom.2020.11.038","volume":"428","author":"X Yin","year":"2021","unstructured":"Yin X, Wu G, Wei J et al (2021) Multi-stage attention spatial-temporal graph networks for traffic prediction. Neurocomputing 428:42\u201353. https:\/\/doi.org\/10.1016\/j.neucom.2020.11.038","journal-title":"Neurocomputing"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-023-05383-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-023-05383-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-023-05383-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,18]],"date-time":"2023-09-18T08:09:08Z","timestamp":1695024548000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-023-05383-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,13]]},"references-count":51,"journal-issue":{"issue":"16","published-print":{"date-parts":[[2023,11]]}},"alternative-id":["5383"],"URL":"https:\/\/doi.org\/10.1007\/s11227-023-05383-0","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"value":"0920-8542","type":"print"},{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,13]]},"assertion":[{"value":"5 May 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 May 2023","order":2,"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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This declaration is not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}