{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T21:11:36Z","timestamp":1780089096731,"version":"3.54.0"},"reference-count":68,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2024,2]]},"DOI":"10.1007\/s10489-024-05291-7","type":"journal-article","created":{"date-parts":[[2024,2,12]],"date-time":"2024-02-12T15:04:52Z","timestamp":1707750292000},"page":"2716-2749","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Attentive graph structure learning embedded in deep spatial-temporal graph neural network for traffic forecasting"],"prefix":"10.1007","volume":"54","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-3963-8374","authenticated-orcid":false,"given":"Pritam","family":"Bikram","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shubhajyoti","family":"Das","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arindam","family":"Biswas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,2,12]]},"reference":[{"key":"5291_CR1","doi-asserted-by":"crossref","first-page":"109166","DOI":"10.1016\/j.knosys.2022.109166","volume":"250","author":"M Wang","year":"2022","unstructured":"Wang M, Wu L, Li M, Wu D, Shi X, Ma C (2022) Meta-learning based spatial-temporal graph attention network for traffic signal control. Knowl-Based Syst 250:109166","journal-title":"Knowl-Based Syst"},{"key":"5291_CR2","doi-asserted-by":"crossref","first-page":"109791","DOI":"10.1016\/j.asoc.2022.109791","volume":"131","author":"P Liu","year":"2022","unstructured":"Liu P, Hendalianpour A, Feylizadeh M, Pedrycz W (2022) Mathematical modeling of vehicle routing problem in omni-channel retailing. Appl Soft Comput 131:109791","journal-title":"Appl Soft Comput"},{"issue":"2","key":"5291_CR3","first-page":"101","volume":"66","author":"A Kumar","year":"2014","unstructured":"Kumar A, Sato Y, Oishi T, Ono S, Ikeuchi K (2014) Improving gps position accuracy by identification of reflected gps signals using range data for modeling of urban structures. Seisan Kenkyu 66(2):101\u2013107","journal-title":"Seisan Kenkyu"},{"issue":"2","key":"5291_CR4","first-page":"91","volume":"65","author":"A Kumar","year":"2013","unstructured":"Kumar A, Banno A, Ono S, Oishi T, Ikeuchi K (2013) Global coordinate adjustment of the 3d survey models under unstable gps condition. Seisan Kenkyu 65(2):91\u201395","journal-title":"Seisan Kenkyu"},{"key":"5291_CR5","doi-asserted-by":"crossref","first-page":"109028","DOI":"10.1016\/j.knosys.2022.109028","volume":"250","author":"W Zhang","year":"2022","unstructured":"Zhang W, Zhu K, Zhang S, Chen Q, Xu J (2022) Dynamic graph convolutional networks based on spatiotemporal data embedding for traffic flow forecasting. Knowl-Based Syst 250:109028","journal-title":"Knowl-Based Syst"},{"issue":"5","key":"5291_CR6","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1111\/0885-9507.00154","volume":"14","author":"D Park","year":"1999","unstructured":"Park D, Rilett LR (1999) Forecasting freeway link travel times with a multilayer feedforward neural network. Comput Aided Civ Infrastruct Eng 14(5):357\u2013367","journal-title":"Comput Aided Civ Infrastruct Eng"},{"key":"5291_CR7","doi-asserted-by":"crossref","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","journal-title":"Transp Res Part C Emerg Technol"},{"key":"5291_CR8","doi-asserted-by":"crossref","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, Yu H, Wang Y (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","journal-title":"Transp Res Part C Emerg Technol"},{"issue":"4","key":"5291_CR9","doi-asserted-by":"crossref","first-page":"818","DOI":"10.3390\/s17040818","volume":"17","author":"X Ma","year":"2017","unstructured":"Ma X, Dai Z, He Z, Ma J, Wang Y, Wang Y (2017) Learning traffic as images: a deep convolutional neural network for large-scale transportation network speed prediction. Sensors 17(4):818","journal-title":"Sensors"},{"key":"5291_CR10","doi-asserted-by":"crossref","first-page":"108990","DOI":"10.1016\/j.knosys.2022.108990","volume":"249","author":"A Khaled","year":"2022","unstructured":"Khaled A, Elsir AMT, Shen Y (2022) Tfgan: traffic forecasting using generative adversarial network with multi-graph convolutional network. Knowl-Based Syst 249:108990","journal-title":"Knowl-Based Syst"},{"issue":"3","key":"5291_CR11","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1023\/B:STCO.0000035301.49549.88","volume":"14","author":"AJ Smola","year":"2004","unstructured":"Smola AJ, Sch\u00f6lkopf B (2004) A tutorial on support vector regression. Stat Comput 14(3):199\u2013222","journal-title":"Stat Comput"},{"key":"5291_CR12","doi-asserted-by":"crossref","first-page":"109788","DOI":"10.1016\/j.asoc.2022.109788","volume":"132","author":"X Han","year":"2023","unstructured":"Han X, Zhu X, Pedrycz W, Li Z (2023) A three-way classification with fuzzy decision trees. Appl Soft Comput 132:109788","journal-title":"Appl Soft Comput"},{"issue":"3","key":"5291_CR13","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1061\/(ASCE)0733-947X(1995)121:3(249)","volume":"121","author":"MM Hamed","year":"1995","unstructured":"Hamed MM, Al-Masaeid HR, Said ZMB (1995) Short-term prediction of traffic volume in urban arterials. J Transp Eng 121(3):249\u2013254","journal-title":"J Transp Eng"},{"issue":"2","key":"5291_CR14","first-page":"865","volume":"16","author":"Y Lv","year":"2014","unstructured":"Lv Y, Duan Y, Kang W, Li Z, Wang F-Y (2014) Traffic flow prediction with big data: a deep learning approach. IEEE Trans Intell Transp Syst 16(2):865\u2013873","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"5","key":"5291_CR15","doi-asserted-by":"crossref","first-page":"2191","DOI":"10.1109\/TITS.2014.2311123","volume":"15","author":"W Huang","year":"2014","unstructured":"Huang W, Song G, Hong H, Xie K (2014) Deep architecture for traffic flow prediction: deep belief networks with multitask learning. IEEE Trans Intell Transp Syst 15(5):2191\u20132201","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"8","key":"5291_CR16","doi-asserted-by":"crossref","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":"3","key":"5291_CR17","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1109\/MCI.2016.2572539","volume":"11","author":"T Chen","year":"2016","unstructured":"Chen T, Xu R, He Y, Xia Y, Wang X (2016) Learning user and product distributed representations using a sequence model for sentiment analysis. IEEE Comput Intell Mag 11(3):34\u201344","journal-title":"IEEE Comput Intell Mag"},{"issue":"7","key":"5291_CR18","doi-asserted-by":"crossref","first-page":"1501","DOI":"10.3390\/s17071501","volume":"17","author":"H Yu","year":"2017","unstructured":"Yu H, Wu Z, Wang S, Wang Y, Ma X (2017) Spatiotemporal recurrent convolutional networks for traffic prediction in transportation networks. Sensors 17(7):1501","journal-title":"Sensors"},{"issue":"11","key":"5291_CR19","doi-asserted-by":"crossref","first-page":"4883","DOI":"10.1109\/TITS.2019.2950416","volume":"21","author":"Z Cui","year":"2019","unstructured":"Cui Z, Henrickson K, Ke R, Wang Y (2019) Traffic graph convolutional recurrent neural network: a deep learning framework for network-scale traffic learning and forecasting. IEEE Trans Intell Transp Syst 21(11):4883\u20134894","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"9","key":"5291_CR20","doi-asserted-by":"crossref","first-page":"3848","DOI":"10.1109\/TITS.2019.2935152","volume":"21","author":"L Zhao","year":"2019","unstructured":"Zhao L, Song Y, Zhang C, Liu Y, Wang P, Lin T, Deng M, Li H (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":"5291_CR21","doi-asserted-by":"crossref","unstructured":"Han Y, Zhao S, Deng H, Jia W (2023) Principal graph embedding convolutional recurrent network for traffic flow prediction. Appl Intell, 1\u201315","DOI":"10.1007\/s10489-022-04211-x"},{"key":"5291_CR22","doi-asserted-by":"crossref","first-page":"102620","DOI":"10.1016\/j.trc.2020.102620","volume":"115","author":"Z Cui","year":"2020","unstructured":"Cui Z, Ke R, Pu Z, Ma X, Wang Y (2020) Learning traffic as a graph: a gated graph wavelet recurrent neural network for network-scale traffic prediction. Transp Res Part C Emerg Technol 115:102620","journal-title":"Transp Res Part C Emerg Technol"},{"key":"5291_CR23","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.ins.2022.11.138","volume":"622","author":"J Liang","year":"2023","unstructured":"Liang J, Tang J, Gao F, Wang Z, Huang H (2023) On region-level travel demand forecasting using multi-task adaptive graph attention network. Inf Sci 622:161\u2013177","journal-title":"Inf Sci"},{"key":"5291_CR24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.trc.2017.02.024","volume":"79","author":"NG Polson","year":"2017","unstructured":"Polson NG, Sokolov VO (2017) Deep learning for short-term traffic flow prediction. Transp Res Part C Emerg Technol 79:1\u201317","journal-title":"Transp Res Part C Emerg Technol"},{"key":"5291_CR25","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1016\/j.future.2018.10.052","volume":"93","author":"F Kong","year":"2019","unstructured":"Kong F, Li J, Jiang B, Song H (2019) Short-term traffic flow prediction in smart multimedia system for internet of vehicles based on deep belief network. Futur Gener Comput Syst 93:460\u2013472","journal-title":"Futur Gener Comput Syst"},{"key":"5291_CR26","doi-asserted-by":"crossref","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, Yu H, Wang Y (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","journal-title":"Transp Res Part C Emerg Technol"},{"key":"5291_CR27","doi-asserted-by":"crossref","first-page":"110237","DOI":"10.1016\/j.knosys.2022.110237","volume":"262","author":"TM Rajeh","year":"2023","unstructured":"Rajeh TM, Li T, Li C, Javed MH, Luo Z, Alhaek F (2023) Modeling multi-regional temporal correlation with gated recurrent unit and multiple linear regression for urban traffic flow prediction. Knowl-Based Syst 262:110237","journal-title":"Knowl-Based Syst"},{"key":"5291_CR28","doi-asserted-by":"crossref","unstructured":"Pham P, Nguyen LT, Nguyen N-T, Pedrycz W, Yun U, Lin JC-W, Vo B (2023) An approach to semantic-aware heterogeneous network embedding for recommender systems. IEEE Trans Cybern","DOI":"10.1109\/TCYB.2022.3233819"},{"key":"5291_CR29","doi-asserted-by":"crossref","first-page":"109054","DOI":"10.1016\/j.knosys.2022.109054","volume":"250","author":"R He","year":"2022","unstructured":"He R, Liu Y, Xiao Y, Lu X, Zhang S (2022) Deep spatio-temporal 3d densenet with multiscale convlstm-resnet network for citywide traffic flow forecasting. Knowl-Based Syst 250:109054","journal-title":"Knowl-Based Syst"},{"key":"5291_CR30","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/j.trpro.2023.02.059","volume":"68","author":"V Shepelev","year":"2023","unstructured":"Shepelev V, Slobodin I, Almetova Z, Nevolin D, Shvecov A (2023) A hybrid traffic forecasting model for urban environments based on convolutional and recurrent neural networks. Transp Res Procedia 68:441\u2013446","journal-title":"Transp Res Procedia"},{"key":"5291_CR31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10489-019-01511-7","volume":"50","author":"Z Liu","year":"2020","unstructured":"Liu Z, Li D, Ge SS, Tian F (2020) Small traffic sign detection from large image. Appl Intell 50:1\u201313","journal-title":"Appl Intell"},{"issue":"10","key":"5291_CR32","doi-asserted-by":"crossref","first-page":"18155","DOI":"10.1109\/TITS.2022.3150600","volume":"23","author":"Y He","year":"2022","unstructured":"He Y, Li L, Zhu X, Tsui KL (2022) Multi-graph convolutional-recurrent neural network (mgc-rnn) for short-term forecasting of transit passenger flow. IEEE Trans Intell Transp Syst 23(10):18155\u201318174","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"5291_CR33","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.ins.2022.07.125","volume":"610","author":"S Cao","year":"2022","unstructured":"Cao S, Wu L, Wu J, Wu D, Li Q (2022) A spatio-temporal sequence-to-sequence network for traffic flow prediction. Inf Sci 610:185\u2013203","journal-title":"Inf Sci"},{"issue":"4","key":"5291_CR34","doi-asserted-by":"crossref","first-page":"3672","DOI":"10.1007\/s10489-022-03739-2","volume":"53","author":"Z Li","year":"2023","unstructured":"Li Z, Zhang Y, Guo D, Zhou X, Wang X, Zhu L (2023) Long-term traffic forecasting based on adaptive graph cross strided convolution network. Appl Intell 53(4):3672\u20133686","journal-title":"Appl Intell"},{"key":"5291_CR35","doi-asserted-by":"crossref","unstructured":"Li H, Yang S, Song Y, Luo Y, Li J, Zhou T (2022) Spatial dynamic graph convolutional network for traffic flow forecasting. Appl Intell, 1\u201313","DOI":"10.1109\/BigData59044.2023.10386250"},{"key":"5291_CR36","doi-asserted-by":"crossref","unstructured":"Kong X, Wei X, Zhang J, Xing W, Lu W (2022) Jointgraph: joint pre-training framework for traffic forecasting with spatial-temporal gating diffusion graph attention network. Appl Intell, 1\u201318","DOI":"10.1007\/s10489-022-04218-4"},{"issue":"1","key":"5291_CR37","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1007\/s10489-022-03501-8","volume":"53","author":"C Zhao","year":"2023","unstructured":"Zhao C, Chang X, Xie T, Fujita H, Wu J (2023) Unsupervised anomaly detection based method of risk evaluation for road traffic accident. Appl Intell 53(1):369\u2013384","journal-title":"Appl Intell"},{"key":"5291_CR38","unstructured":"Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv:1609.02907"},{"issue":"3","key":"5291_CR39","doi-asserted-by":"crossref","first-page":"2763","DOI":"10.1007\/s10489-021-02587-w","volume":"52","author":"K-HN Bui","year":"2022","unstructured":"Bui K-HN, Cho J, Yi H (2022) Spatial-temporal graph neural network for traffic forecasting: an overview and open research issues. Appl Intell 52(3):2763\u20132774","journal-title":"Appl Intell"},{"key":"5291_CR40","doi-asserted-by":"crossref","unstructured":"Liu J, Kang Y, Li H, Wang H, Yang X (2022) Stghtn: Spatial-temporal gated hybrid transformer network for traffic flow forecasting. Appl Intell, 1\u201317","DOI":"10.1007\/s10489-022-04122-x"},{"key":"5291_CR41","doi-asserted-by":"crossref","unstructured":"Wang X, Wang Y, Peng J, Zhang Z, Tang X (2022) A hybrid framework for multivariate long-sequence time series forecasting. Appl Intell, 1\u201320","DOI":"10.1007\/s10489-022-04110-1"},{"key":"5291_CR42","doi-asserted-by":"crossref","unstructured":"Zhang Y, Yang Y, Zhou W, Wang H, Ouyang X (2021) Multi-city traffic flow forecasting via multi-task learning. Appl Intell, 1\u201319","DOI":"10.1007\/s10489-020-02074-8"},{"key":"5291_CR43","doi-asserted-by":"crossref","first-page":"580","DOI":"10.1016\/j.ins.2022.11.086","volume":"621","author":"Y Xu","year":"2023","unstructured":"Xu Y, Cai X, Wang E, Liu W, Yang Y, Yang F (2023) Dynamic traffic correlations based spatio-temporal graph convolutional network for urban traffic prediction. Inf Sci 621:580\u2013595","journal-title":"Inf Sci"},{"key":"5291_CR44","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1016\/j.ins.2022.11.168","volume":"622","author":"Z Qiu","year":"2023","unstructured":"Qiu Z, Zhu T, Jin Y, Sun L, Du B (2023) A graph attention fusion network for event-driven traffic speed prediction. Inf Sci 622:405\u2013423","journal-title":"Inf Sci"},{"key":"5291_CR45","doi-asserted-by":"crossref","unstructured":"Kong X, Zhang J, Wei X, Xing W, Lu W (2022) Adaptive spatial-temporal graph attention networks for traffic flow forecasting. Appl Intell, 1\u201317","DOI":"10.1007\/s10489-021-02648-0"},{"issue":"6","key":"5291_CR46","doi-asserted-by":"crossref","first-page":"4483","DOI":"10.1007\/s10462-021-10004-4","volume":"54","author":"M Huisman","year":"2021","unstructured":"Huisman M, Van Rijn JN, Plaat A (2021) A survey of deep meta-learning. Artif Intell Rev 54(6):4483\u20134541","journal-title":"Artif Intell Rev"},{"key":"5291_CR47","doi-asserted-by":"crossref","unstructured":"Zhang C-Y, Cai H-C, Chen CP, Lin Y-N, Fang W-P (2023) Graph representation learning with adaptive metric. IEEE Trans Netw Sci Eng","DOI":"10.1109\/TNSE.2023.3239661"},{"key":"5291_CR48","doi-asserted-by":"crossref","unstructured":"Zhang X, Song D, Tao D (2023) Ricci curvature-based graph sparsification for continual graph representation learning. IEEE Trans Neural Netw Learn Syst","DOI":"10.1109\/TNNLS.2023.3303454"},{"key":"5291_CR49","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/j.neucom.2022.01.064","volume":"495","author":"J Yuan","year":"2022","unstructured":"Yuan J, Cao M, Cheng H, Yu H, Xie J, Wang C (2022) A unified structure learning framework for graph attention networks. Neurocomputing 495:194\u2013204","journal-title":"Neurocomputing"},{"key":"5291_CR50","doi-asserted-by":"crossref","unstructured":"Guo T, Hou F, Pang Y, Jia X, Wang Z, Wang R (2023) Learning and integration of adaptive hybrid graph structures for multivariate time series forecasting. Inf Sci 119560","DOI":"10.1016\/j.ins.2023.119560"},{"key":"5291_CR51","doi-asserted-by":"crossref","unstructured":"Peng C, Hou X, Chen Y, Kang Z, Chen C, Cheng Q (2023) Global and local similarity learning in multi-kernel space for nonnegative matrix factorization. Knowl-Based Syst 110946","DOI":"10.1016\/j.knosys.2023.110946"},{"key":"5291_CR52","doi-asserted-by":"crossref","first-page":"108199","DOI":"10.1016\/j.knosys.2022.108199","volume":"242","author":"X Ta","year":"2022","unstructured":"Ta X, Liu Z, Hu X, Yu L, Sun L, Du B (2022) Adaptive spatio-temporal graph neural network for traffic forecasting. Knowl-Based Syst 242:108199","journal-title":"Knowl-Based Syst"},{"key":"5291_CR53","doi-asserted-by":"crossref","unstructured":"Gama F, Bruna J, Ribeiro A (2020) Stability properties of graph neural networks. Trans Signal Process 68:5680\u20135695","DOI":"10.1109\/TSP.2020.3026980"},{"key":"5291_CR54","unstructured":"Defferrard M, Bresson X, Vandergheynst P (2016) Convolutional neural networks on graphs with fast localized spectral filtering. Adv Neural Inf Process 29"},{"issue":"7","key":"5291_CR55","doi-asserted-by":"crossref","first-page":"5468","DOI":"10.1109\/JIOT.2020.3042090","volume":"8","author":"M Tariq","year":"2020","unstructured":"Tariq M, Ali M, Naeem F, Poor HV (2020) Vulnerability assessment of 6g-enabled smart grid cyber-physical systems. IEEE Internet Things J 8(7):5468\u20135475","journal-title":"IEEE Internet Things J"},{"issue":"1","key":"5291_CR56","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1109\/TFUZZ.2020.2986982","volume":"29","author":"M Ali","year":"2020","unstructured":"Ali M, Adnan M, Tariq M, Poor HV (2020) Load forecasting through estimated parametrized based fuzzy inference system in smart grids. IEEE Trans Fuzzy Syst 29(1):156\u2013165","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"4","key":"5291_CR57","doi-asserted-by":"crossref","first-page":"4189","DOI":"10.1109\/TITS.2022.3233890","volume":"24","author":"A Wang","year":"2023","unstructured":"Wang A, Ye Y, Song X, Zhang S, James J (2023) Traffic prediction with missing data: a multi-task learning approach. IEEE Trans Intell Transp Syst 24(4):4189\u20134202","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"5291_CR58","doi-asserted-by":"crossref","unstructured":"Chauhan S, Singh M, Aggarwal AK (2023) Investigative analysis of different mutation on diversity-driven multi-parent evolutionary algorithm and its application in area coverage optimization of wsn. Soft Comput 1\u201327","DOI":"10.1007\/s00500-023-08090-3"},{"key":"5291_CR59","doi-asserted-by":"crossref","first-page":"105803","DOI":"10.1016\/j.engappai.2022.105803","volume":"119","author":"S Chauhan","year":"2023","unstructured":"Chauhan S, Singh M, Aggarwal AK (2023) Designing of optimal digital iir filter in the multi-objective framework using an evolutionary algorithm. Eng Appl Artif Intell 119:105803","journal-title":"Eng Appl Artif Intell"},{"issue":"3","key":"5291_CR60","first-page":"82","volume":"21","author":"J Liu","year":"2004","unstructured":"Liu J, Guan W (2004) A summary of traffic flow forecasting methods. J Highway Transp Res Dev 21(3):82\u201385","journal-title":"J Highway Transp Res Dev"},{"key":"5291_CR61","unstructured":"Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. Adv Neural Inf Process 27"},{"issue":"1","key":"5291_CR62","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1109\/JAS.2023.123033","volume":"10","author":"Z Wei","year":"2023","unstructured":"Wei Z, Zhao H, Li Z, Bu X, Chen Y, Zhang X, Lv Y, Wang F-Y (2023) Stgsa: a novel spatial-temporal graph synchronous aggregation model for traffic prediction. IEEE\/CAA J Autom Sin 10(1):226\u2013238","journal-title":"IEEE\/CAA J Autom Sin"},{"key":"5291_CR63","doi-asserted-by":"crossref","unstructured":"Kumar R, Mendes\u00a0Moreira J, Chandra J (2023) Dygcn-lstm: a dynamic gcn-lstm based encoder-decoder framework for multistep traffic prediction. Appl Intell 1\u201324","DOI":"10.1007\/s10489-023-04871-3"},{"key":"5291_CR64","doi-asserted-by":"crossref","first-page":"128525","DOI":"10.1016\/j.physa.2023.128525","volume":"614","author":"S Liu","year":"2023","unstructured":"Liu S, Feng X, Ren Y, Jiang H, Yu H (2023) Dcenet: A dynamic correlation evolve network for short-term traffic prediction. Phys A Stat Mech Appl 614:128525","journal-title":"Phys A Stat Mech Appl"},{"key":"5291_CR65","doi-asserted-by":"crossref","first-page":"35973","DOI":"10.1109\/ACCESS.2021.3062114","volume":"9","author":"J Zhu","year":"2021","unstructured":"Zhu J, Wang Q, Tao C, Deng H, Zhao L, Li H (2021) Ast-gcn: Attribute-augmented spatiotemporal graph convolutional network for traffic forecasting. IEEE Access 9:35973\u201335983","journal-title":"IEEE Access"},{"issue":"11","key":"5291_CR66","doi-asserted-by":"crossref","first-page":"20681","DOI":"10.1109\/TITS.2022.3173689","volume":"23","author":"J Huang","year":"2022","unstructured":"Huang J, Luo K, Cao L, Wen Y, Zhong S (2022) Learning multiaspect traffic couplings by multirelational graph attention networks for traffic prediction. IEEE Trans Intell Transp Syst 23(11):20681\u201320695","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"2","key":"5291_CR67","doi-asserted-by":"crossref","first-page":"1456","DOI":"10.1109\/TITS.2020.3026836","volume":"23","author":"Z Li","year":"2020","unstructured":"Li Z, Xiong G, Tian Y, Lv Y, Chen Y, Hui P, Su X (2020) A multi-stream feature fusion approach for traffic prediction. EEE Trans Intell Transp Syst 23(2):1456\u20131466","journal-title":"EEE Trans Intell Transp Syst"},{"key":"5291_CR68","first-page":"17804","volume":"33","author":"L Bai","year":"2020","unstructured":"Bai L, Yao L, Li C, Wang X, Wang C (2020) Adaptive graph convolutional recurrent network for traffic forecasting. Adv Neural Inf Process 33:17804\u201317815","journal-title":"Adv Neural Inf Process"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05291-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-024-05291-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05291-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,14]],"date-time":"2024-03-14T14:21:41Z","timestamp":1710426101000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-024-05291-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2]]},"references-count":68,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,2]]}},"alternative-id":["5291"],"URL":"https:\/\/doi.org\/10.1007\/s10489-024-05291-7","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2]]},"assertion":[{"value":"20 January 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 February 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics standard"}},{"value":"The authors declare no conflict of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interests"}}]}}