{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:24:31Z","timestamp":1740122671589,"version":"3.37.3"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,12,24]],"date-time":"2024-12-24T00:00:00Z","timestamp":1734998400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,24]],"date-time":"2024-12-24T00:00:00Z","timestamp":1734998400000},"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":["52272367"],"award-info":[{"award-number":["52272367"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1007\/s10489-024-06117-2","type":"journal-article","created":{"date-parts":[[2024,12,24]],"date-time":"2024-12-24T01:07:51Z","timestamp":1735002471000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Short-term traffic flow prediction based on spatial\u2013temporal attention time gated convolutional network with particle swarm optimization"],"prefix":"10.1007","volume":"55","author":[{"given":"Zhongxing","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zenan","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chaofeng","family":"Pan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,24]]},"reference":[{"issue":"14","key":"6117_CR1","doi-asserted-by":"publisher","first-page":"16104","DOI":"10.1007\/s10489-021-03022-w","volume":"52","author":"L Liao","year":"2022","unstructured":"Liao L, Hu Z, Zheng Y, Bi S, Zou F, Qiu H, Zhang M (2022) An improved dynamic Chebyshev graph convolution network for traffic flow prediction with spatial-temporal attention. Appl Intell 52(14):16104\u201316116","journal-title":"Appl Intell"},{"key":"6117_CR2","doi-asserted-by":"publisher","first-page":"3252","DOI":"10.1007\/s10489-020-01716-1","volume":"50","author":"A Belhadi","year":"2020","unstructured":"Belhadi A, Djenouri Y, Djenouri D, Lin JC-WJAI (2020) A recurrent neural network for urban long-term traffic flow forecasting. Appl Intell 50:3252\u20133265","journal-title":"Appl Intell"},{"key":"6117_CR3","doi-asserted-by":"publisher","first-page":"582","DOI":"10.1016\/j.proeng.2017.04.417","volume":"187","author":"SV Kumar","year":"2017","unstructured":"Kumar SV (2017) Traffic flow prediction using Kalman filtering technique. Procedia Eng 187:582\u2013587","journal-title":"Procedia Eng"},{"issue":"3","key":"6117_CR4","doi-asserted-by":"publisher","first-page":"1552","DOI":"10.1080\/23249935.2020.1764662","volume":"16","author":"S Shahriari","year":"2020","unstructured":"Shahriari S, Ghasri M, Sisson SA, Rashidi T (2020) Ensemble of ARIMA: combining parametric and bootstrapping technique for traffic flow prediction. Transportmetrica A: Transport Sci 16(3):1552\u20131573","journal-title":"Transportmetrica A: Transport Sci"},{"key":"6117_CR5","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.proeng.2016.01.234","volume":"137","author":"Y Cong","year":"2016","unstructured":"Cong Y, Wang J, Li X (2016) Traffic flow forecasting by a least squares support vector machine with a fruit fly optimization algorithm. Procedia Eng 137:59\u201368","journal-title":"Procedia Eng"},{"key":"6117_CR6","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1016\/j.ins.2022.06.090","volume":"608","author":"G Lin","year":"2022","unstructured":"Lin G, Lin A, Gu D (2022) Using support vector regression and K-nearest neighbors for short-term traffic flow prediction based on maximal information coefficient. Inf Sci 608:517\u2013531","journal-title":"Inf Sci"},{"issue":"1","key":"6117_CR7","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1080\/23249935.2018.1491073","volume":"16","author":"D Xu","year":"2020","unstructured":"Xu D, Wang Y, Peng P, Beilun S, Deng Z, Guo H (2020) Real-time road traffic state prediction based on kernel-KNN. Transportmetrica A: Transport Sci 16(1):104\u2013118","journal-title":"Transportmetrica A: Transport Sci"},{"key":"6117_CR8","doi-asserted-by":"crossref","unstructured":"Liu F, Wei Z, Huang Z, Lu Y, Hu X, Shi L (2019) A multi-grouped ls-svm method for short-term urban traffic flow prediction. In: 2019 IEEE Global Communications Conference (GLOBECOM). IEEE, pp 1\u20136","DOI":"10.1109\/GLOBECOM38437.2019.9013761"},{"key":"6117_CR9","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1016\/j.aej.2023.05.015","volume":"74","author":"G Dai","year":"2023","unstructured":"Dai G, Tang J, Luo W (2023) Short-term traffic flow prediction: An ensemble machine learning approach. Alex Eng J 74:467\u2013480","journal-title":"Alex Eng J"},{"key":"6117_CR10","doi-asserted-by":"publisher","first-page":"103746","DOI":"10.1016\/j.jnca.2023.103746","volume":"220","author":"Y Guo","year":"2023","unstructured":"Guo Y, Peng Y, Hao R, Tang X (2023) Capturing spatial\u2013temporal correlations with Attention based Graph Convolutional Network for network traffic prediction. J Netw Comput Appl 220:103746","journal-title":"J Netw Comput Appl"},{"issue":"24","key":"6117_CR11","doi-asserted-by":"publisher","first-page":"30843","DOI":"10.1007\/s10489-023-05053-x","volume":"53","author":"D Bai","year":"2023","unstructured":"Bai D, Xia D, Huang D, Hu Y, Li Y, Li H (2023) Spatial-temporal graph neural network based on gated convolution and topological attention for traffic flow prediction. Appl Intell 53(24):30843\u201330864","journal-title":"Appl Intell"},{"key":"6117_CR12","doi-asserted-by":"publisher","first-page":"102228","DOI":"10.1016\/j.inffus.2024.102228","volume":"105","author":"Z Geng","year":"2024","unstructured":"Geng Z, Xu J, Wu R, Zhao C, Wang J, Li Y, Zhang C (2024) STGAFormer: Spatial\u2013temporal gated attention transformer based graph neural network for traffic flow forecasting. Information Fusion 105:102228","journal-title":"Information Fusion"},{"issue":"2","key":"6117_CR13","doi-asserted-by":"publisher","first-page":"2001","DOI":"10.32604\/iasc.2023.035799","volume":"37","author":"F Wei","year":"2023","unstructured":"Wei F, Li X, Guo Y, Wang Z, Li Q, Ma X (2023) Flow direction level traffic flow prediction based on a GCN-LSTM combined model. Intell Automation Soft Computing 37(2):2001\u20132018","journal-title":"Intell Automation Soft Computing"},{"key":"6117_CR14","doi-asserted-by":"publisher","first-page":"94051","DOI":"10.1109\/ACCESS.2022.3204036","volume":"10","author":"H Zheng","year":"2022","unstructured":"Zheng H, Li X, Li Y, Yan Z, Li T (2022) GCN-GAN: integrating graph convolutional network and generative adversarial network for traffic flow prediction. IEEE Access 10:94051\u201394062","journal-title":"IEEE Access"},{"key":"6117_CR15","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1016\/j.ins.2023.03.093","volume":"634","author":"Y Bao","year":"2023","unstructured":"Bao Y, Liu J, Shen Q, Cao Y, Ding W, Shi Q (2023) PKET-GCN: prior knowledge enhanced time-varying graph convolution network for traffic flow prediction. Inf Sci 634:359\u2013381","journal-title":"Inf Sci"},{"issue":"2","key":"6117_CR16","doi-asserted-by":"publisher","first-page":"1009","DOI":"10.1109\/TITS.2020.3019497","volume":"23","author":"K Guo","year":"2020","unstructured":"Guo K, Hu Y, Qian Z, Sun Y, Gao J, Yin B (2020) Dynamic graph convolution network for traffic forecasting based on latent network of Laplace matrix estimation. IEEE Trans Intell Transp Syst 23(2):1009\u20131018","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"6117_CR17","doi-asserted-by":"publisher","first-page":"103659","DOI":"10.1016\/j.trc.2022.103659","volume":"139","author":"W Zhang","year":"2022","unstructured":"Zhang W, Zhu F, Lv Y, Tan C, Liu W, Zhang X, Wang F-Y (2022) AdapGL: An adaptive graph learning algorithm for traffic prediction based on spatiotemporal neural networks. Transportation Res Part C: Emerging Technologies 139:103659","journal-title":"Transportation Res Part C: Emerging Technologies"},{"key":"6117_CR18","doi-asserted-by":"crossref","unstructured":"Sun L, Liu M, Liu G, Chen X, Yu X (2024) FD-TGCN: fast and dynamic temporal graph convolution network for traffic flow prediction. Inf Fusion 106:102291","DOI":"10.1016\/j.inffus.2024.102291"},{"key":"6117_CR19","doi-asserted-by":"publisher","first-page":"102079","DOI":"10.1016\/j.inffus.2023.102079","volume":"103","author":"S Liu","year":"2024","unstructured":"Liu S, He M, Wu Z, Lu P, Gu W (2024) Spatial\u2013temporal graph neural network traffic prediction based load balancing with reinforcement learning in cellular networks. Information Fusion 103:102079","journal-title":"Information Fusion"},{"key":"6117_CR20","doi-asserted-by":"publisher","first-page":"102513","DOI":"10.1016\/j.displa.2023.102513","volume":"80","author":"H Xing","year":"2023","unstructured":"Xing H, Chen A, Zhang X (2023) RL-GCN: Traffic flow prediction based on graph convolution and reinforcement learning for smart cities. Displays 80:102513","journal-title":"Displays"},{"key":"6117_CR21","doi-asserted-by":"crossref","unstructured":"Liu Z, Ding F, Dai Y, Li L, Chen T, Tan H (2024) Spatial-temporal graph convolution network model with traffic fundamental diagram information informed for network traffic flow prediction. Expert Syst Appl 249:123543","DOI":"10.1016\/j.eswa.2024.123543"},{"key":"6117_CR22","doi-asserted-by":"publisher","first-page":"108135","DOI":"10.1016\/j.engappai.2024.108135","volume":"133","author":"Y Bao","year":"2024","unstructured":"Bao Y, Shen Q, Cao Y, Ding W, Shi Q (2024) Residual attention enhanced Time-varying Multi-Factor Graph Convolutional Network for traffic flow prediction. Eng Appl Artif Intell 133:108135","journal-title":"Eng Appl Artif Intell"},{"key":"6117_CR23","doi-asserted-by":"publisher","first-page":"102146","DOI":"10.1016\/j.inffus.2023.102146","volume":"104","author":"J Chen","year":"2024","unstructured":"Chen J, Zheng L, Hu Y, Wang W, Zhang H, Hu X (2024) Traffic flow matrix-based graph neural network with attention mechanism for traffic flow prediction. Information Fusion 104:102146","journal-title":"Information Fusion"},{"key":"6117_CR24","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1016\/j.neunet.2023.12.016","volume":"171","author":"Y Luo","year":"2024","unstructured":"Luo Y, Zheng J, Wang X, Tao Y, Jiang X (2024) GT-LSTM: A spatio-temporal ensemble network for traffic flow prediction. Neural Netw 171:251\u2013262","journal-title":"Neural Netw"},{"key":"6117_CR25","doi-asserted-by":"publisher","first-page":"107210","DOI":"10.1016\/j.icheatmasstransfer.2023.107210","volume":"150","author":"F Feng","year":"2024","unstructured":"Feng F, Li Y-B, Chen Z-H, Wu W-T, Peng J-Z, Mei M (2024) Rapid optimization for inner thermal layout in horizontal annuli using genetic algorithm coupled graph convolutional neural network. Int Commun Heat Mass Transfer 150:107210","journal-title":"Int Commun Heat Mass Transfer"},{"key":"6117_CR26","first-page":"2127","volume":"25","author":"S Xu","year":"2023","unstructured":"Xu S, Xu X, Jia W, Liu W, Li J, Li D (2023) Microstructure-property mapping modeling for AZ31 alloy rolling deformation using improved PSO-BP neural network. J Market Res 25:2127\u20132139","journal-title":"J Market Res"},{"key":"6117_CR27","doi-asserted-by":"publisher","first-page":"108882","DOI":"10.1016\/j.est.2023.108882","volume":"73","author":"Z Pei","year":"2023","unstructured":"Pei Z, Liu K, Zhang S, Chen X (2023) Optimized EKF algorithm using TSO-BP neural network for lithium battery state of charge estimation. J Energy Storage 73:108882","journal-title":"J Energy Storage"},{"key":"6117_CR28","doi-asserted-by":"publisher","first-page":"103813","DOI":"10.1016\/j.csite.2023.103813","volume":"53","author":"J Guo","year":"2024","unstructured":"Guo J, Chen C, Wen H, Cai G, Liu Y (2024) Prediction model of goaf coal temperature based on PSO-GRU deep neural network. Case Studies in Thermal Eng 53:103813","journal-title":"Case Studies in Thermal Eng"},{"key":"6117_CR29","doi-asserted-by":"publisher","first-page":"110806","DOI":"10.1016\/j.est.2024.110806","volume":"84","author":"F Li","year":"2024","unstructured":"Li F, Zuo W, Zhou K, Li Q, Huang Y (2024) State of charge estimation of lithium-ion batteries based on PSO-TCN-Attention neural network. Journal of Energy Storage 84:110806","journal-title":"Journal of Energy Storage"},{"key":"6117_CR30","doi-asserted-by":"crossref","unstructured":"Sahib M-M, Kov\u00e1cs G (2024) Multi-objective optimization of composite sandwich structures using Artificial Neural Networks and Genetic Algorithm. Results Eng 21:101937","DOI":"10.1016\/j.rineng.2024.101937"},{"key":"6117_CR31","doi-asserted-by":"publisher","first-page":"112700","DOI":"10.1016\/j.matdes.2024.112700","volume":"238","author":"J Lee","year":"2024","unstructured":"Lee J, Park D, Park K, Song H, Kim T-S, Ryu S (2024) Optimization of grid composite configuration to maximize toughness using integrated hierarchical deep neural network and genetic algorithm. Mater Des 238:112700","journal-title":"Mater Des"},{"key":"6117_CR32","doi-asserted-by":"publisher","first-page":"129448","DOI":"10.1016\/j.physa.2023.129448","volume":"634","author":"B Naheliya","year":"2024","unstructured":"Naheliya B, Redhu P, Kumar K (2024) MFOA-Bi-LSTM: An optimized bidirectional long short-term memory model for short-term traffic flow prediction. Physica A 634:129448","journal-title":"Physica A"},{"key":"6117_CR33","doi-asserted-by":"publisher","first-page":"129001","DOI":"10.1016\/j.physa.2023.129001","volume":"625","author":"P Redhu","year":"2023","unstructured":"Redhu P, Kumar K (2023) Short-term traffic flow prediction based on optimized deep learning neural network: PSO-Bi-LSTM. Physica A 625:129001","journal-title":"Physica A"},{"key":"6117_CR34","doi-asserted-by":"publisher","first-page":"102858","DOI":"10.1016\/j.scs.2021.102858","volume":"69","author":"W Du","year":"2021","unstructured":"Du W, Zhang Q, Chen Y, Ye Z (2021) An urban short-term traffic flow prediction model based on wavelet neural network with improved whale optimization algorithm. Sustain Cities Soc 69:102858","journal-title":"Sustain Cities Soc"},{"key":"6117_CR35","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1016\/j.apm.2021.09.040","volume":"102","author":"H Yan","year":"2022","unstructured":"Yan H, Qi Y, Yu D-J (2022) Short-term traffic flow prediction based on a hybrid optimization algorithm. Appl Math Model 102:385\u2013404","journal-title":"Appl Math Model"},{"issue":"9","key":"6117_CR36","doi-asserted-by":"publisher","first-page":"16752","DOI":"10.1109\/TITS.2022.3195605","volume":"23","author":"Q Ke","year":"2022","unstructured":"Ke Q, Si\u0142ka J, Wieczorek M, Bai Z, Wo\u017aniak M (2022) Deep neural network heuristic hierarchization for cooperative intelligent transportation fleet management. IEEE Trans Intell Transp Syst 23(9):16752\u201316762","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"6117_CR37","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, Song C, Wan H (2019) Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. Proceedings of the AAAI Conference Artificial Intell 33:922\u2013929","journal-title":"Proceedings of the AAAI Conference Artificial Intell"},{"issue":"1","key":"6117_CR38","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1007\/s11063-024-11479-2","volume":"56","author":"L Xiong","year":"2024","unstructured":"Xiong L, Yuan X, Hu Z, Huang X, Huang P (2024) Gated Fusion Adaptive Graph Neural Network for Urban Road Traffic Flow Prediction. Neural Process Lett 56(1):9","journal-title":"Neural Process Lett"},{"key":"6117_CR39","first-page":"273","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes C, Vapnik V (1995) Support-vector networks Machine learning 20:273\u2013297","journal-title":"Support-vector networks Machine learning"},{"issue":"8","key":"6117_CR40","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"},{"key":"6117_CR41","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 preprint arXiv:1709.04875","DOI":"10.24963\/ijcai.2018\/505"},{"issue":"13","key":"6117_CR42","doi-asserted-by":"publisher","first-page":"15026","DOI":"10.1007\/s10489-022-03224-w","volume":"52","author":"Q Ni","year":"2022","unstructured":"Ni Q, Zhang M (2022) STGMN: A gated multi-graph convolutional network framework for traffic flow prediction. Appl Intell 52(13):15026\u201315039","journal-title":"Appl Intell"},{"key":"6117_CR43","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 Syst 33:17804\u201317815","journal-title":"Adv Neural Inf Process Syst"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-06117-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-024-06117-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-06117-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,30]],"date-time":"2025-01-30T16:05:21Z","timestamp":1738253121000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-024-06117-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,24]]},"references-count":43,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["6117"],"URL":"https:\/\/doi.org\/10.1007\/s10489-024-06117-2","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2024,12,24]]},"assertion":[{"value":"24 November 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 December 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Ethical and informed consent for data used.The data used in this study come from the California State Traffic Performance Measurement System (PEMS) public datasets, designed for transportation research. They do not contain any personally identifiable information or sensitive data. No specific informed consent was required for this study as the data used were obtained from publicly accessible sources which do not contain information related to individual human subjects.","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":"Competing interests"}}],"article-number":"214"}}