{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T10:10:06Z","timestamp":1781086206293,"version":"3.54.1"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2021,7,20]],"date-time":"2021-07-20T00:00:00Z","timestamp":1626739200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,7,20]],"date-time":"2021-07-20T00:00:00Z","timestamp":1626739200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61876017"],"award-info":[{"award-number":["61876017"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61876018"],"award-info":[{"award-number":["61876018"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61906014"],"award-info":[{"award-number":["61906014"]}],"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":[[2022,3]]},"DOI":"10.1007\/s10489-021-02648-0","type":"journal-article","created":{"date-parts":[[2021,7,20]],"date-time":"2021-07-20T19:26:20Z","timestamp":1626809180000},"page":"4300-4316","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":59,"title":["Adaptive spatial-temporal graph attention networks for traffic flow forecasting"],"prefix":"10.1007","volume":"52","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3606-081X","authenticated-orcid":false,"given":"Xiangyuan","family":"Kong","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiang","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiwei","family":"Xing","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,7,20]]},"reference":[{"issue":"2","key":"2648_CR1","first-page":"865","volume":"16","author":"Y Lv","year":"2014","unstructured":"Lv Y, Duan Y, Kang W, Li Z, Wang FY (2014) Traffic flow prediction with big data: a deep learning approach. IEEE Trans Intell Transp Syst 16(2):865","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"2648_CR2","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1016\/j.trc.2018.12.004","volume":"99","author":"Y Wang","year":"2019","unstructured":"Wang Y, Zhang D, Liu Y, Dai B, Lee LH (2019) Enhancing transportation systems via deep learning: A survey. Transp Res C Emerg Technol 99:144. https:\/\/doi.org\/10.1016\/j.trc.2018.12.004","journal-title":"Transp Res C Emerg Technol"},{"issue":"7","key":"2648_CR3","doi-asserted-by":"publisher","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":"8","key":"2648_CR4","doi-asserted-by":"publisher","first-page":"2123","DOI":"10.1109\/TITS.2015.2513411","volume":"17","author":"H Tan","year":"2016","unstructured":"Tan H, Wu Y, Shen B, Jin PJ, Ran B (2016) Short-term traffic prediction based on dynamic tensor completion. IEEE Trans Intell Transp Syst 17(8):2123","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"4","key":"2648_CR5","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, 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":"2648_CR6","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.artint.2018.03.002","volume":"259","author":"J Zhang","year":"2018","unstructured":"Zhang J, Zheng Y, Qi D, Li R, Yi X, Li T (2018) Predicting citywide crowd flows using deep spatio-temporal residual networks. Artif Intell 259:147","journal-title":"Artif Intell"},{"key":"2648_CR7","unstructured":"Kipf TN, Welling M (2017) Semi-Supervised classification with graph convolutional networks. In: International conference on learning representations (ICLR)"},{"key":"2648_CR8","unstructured":"Veli\u010dkovi\u0107 P, Cucurull G, Casanova A, Romero A, Li\u00f2 P, Bengio Y (2018) Graph attention networks. In: International conference on learning representations. https:\/\/openreview.net\/forum?id=rJXMpikCZ"},{"key":"2648_CR9","doi-asserted-by":"crossref","unstructured":"Seo Y, Defferrard M, Vandergheynst P, Bresson X (2018) Structured sequence modeling with graph convolutional recurrent networks. In: Cheng L, Leung ACS, Ozawa S (eds) Neural information processing. Springer International Publishing, Cham, pp 362\u2013373","DOI":"10.1007\/978-3-030-04167-0_33"},{"key":"2648_CR10","unstructured":"Li Y, Yu R, Shahabi C, Liu Y (2018) Diffusion convolutional recurrent neural network: data-driven traffic forecasting. https:\/\/openreview.net\/forum?id=SJiHXGWAZ"},{"key":"2648_CR11","doi-asserted-by":"crossref","unstructured":"Pan Z, Liang Y, Wang W, Yu Y, Zheng Y, Zhang J (2019) Urban traffic prediction from spatio-temporal data using deep meta learning. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp 1720\u20131730","DOI":"10.1145\/3292500.3330884"},{"key":"2648_CR12","doi-asserted-by":"crossref","unstructured":"Yu B, Yin H, Zhu Z (2018) Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. pp 3634\u20133640. http:\/\/dl.acm.org\/citation.cfm?id=3304222.3304273","DOI":"10.24963\/ijcai.2018\/505"},{"key":"2648_CR13","doi-asserted-by":"crossref","unstructured":"Wu Z, Pan S, Long G, Jiang J, Zhang C (2019) Graph WaveNet for deep spatial-temporal graph modeling. In: The 28th international joint conference on artificial intelligence (IJCAI) (International Joint Conferences on Artificial Intelligence Organization","DOI":"10.24963\/ijcai.2019\/264"},{"issue":"4","key":"2648_CR14","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1109\/MPRV.2008.80","volume":"7","author":"M Haklay","year":"2008","unstructured":"Haklay M, Weber P (2008) Openstreetmap: User-generated street maps. IEEE Pervasive Comput 7(4):12","journal-title":"IEEE Pervasive Comput"},{"key":"2648_CR15","unstructured":"Yu B, Li M, Zhang J, Zhu Z (2019) 3d graph convolutional networks with temporal graphs: A spatial information free framework for traffic forecasting. arXiv:1903.00919"},{"key":"2648_CR16","unstructured":"Li M, Zhu Z (2020) Spatial-temporal fusion graph neural networks for traffic flow forecasting. arXiv:2012.09641"},{"key":"2648_CR17","doi-asserted-by":"crossref","unstructured":"Geng X, Li Y, Wang L, Zhang L, Yang Q, Ye J, Liu Y (2019) Spatiotemporal multi-graph convolution network for ride-hailing demand forecasting. In: Proceedings of the AAAI conference on artificial intelligence, vol. 33. pp 3656\u20133663","DOI":"10.1609\/aaai.v33i01.33013656"},{"key":"2648_CR18","unstructured":"Chen Y, Wu L, Zaki M (2020) Iterative deep graph learning for graph neural networks: better and robust node embeddings. Adv Neural Inf Process Syst 33"},{"key":"2648_CR19","unstructured":"Franceschi L, Niepert M, Pontil M, He X (2019) Learning discrete structures for graph neural networks. In: Chaudhuri K, Salakhutdinov R (eds) Proceedings of the 36th international conference on machine learning, proceedings of machine learning research, (PMLR). http:\/\/proceedings.mlr.press\/v97\/franceschi19a.html, vol 97, pp 1972\u20131982"},{"issue":"1","key":"2648_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s41109-019-0194-4","volume":"4","author":"Z Zhang","year":"2019","unstructured":"Zhang Z, Zhao Y, Liu J, Wang S, Tao R, Xin R, Zhang J (2019) A general deep learning framework for network reconstruction and dynamics learning. Appl Netw Sci 4(1):1","journal-title":"Appl Netw Sci"},{"key":"2648_CR21","unstructured":"Jang E, Gu S, Poole B. (2016) Categorical reparameterization with gumbel-softmax. arXiv:1611.01144"},{"key":"2648_CR22","unstructured":"Shazeer N, Lan Z, Cheng Y, Ding N, Hou L (2020) Talking-heads attention. arXiv:2003.02436"},{"key":"2648_CR23","doi-asserted-by":"crossref","unstructured":"Abbasi M, Shahraki A, Taherkordi A (2021) Deep learning for network traffic monitoring and analysis (ntma): a survey. Comput Commun","DOI":"10.1016\/j.comcom.2021.01.021"},{"key":"2648_CR24","doi-asserted-by":"publisher","first-page":"107974","DOI":"10.1016\/j.comnet.2021.107974","volume":"190","author":"K Lin","year":"2021","unstructured":"Lin K, Xu X, Gao H (2021) TSCRNN: A novel classification scheme of encrypted traffic based on flow spatiotemporal features for efficient management of IIoT. Comput Netw 190:107974","journal-title":"Comput Netw"},{"key":"2648_CR25","doi-asserted-by":"crossref","unstructured":"Gao H, Liu C, Li Y, Yang X (2020) V2VR: reliable hybrid-network-oriented V2V data transmission and routing considering RSUs and connectivity probability. IEEE Trans Intell Transp Syst","DOI":"10.1109\/TITS.2020.2983835"},{"key":"2648_CR26","doi-asserted-by":"crossref","unstructured":"Nishi T, Otaki K, Hayakawa K, Yoshimura T (2018) Traffic signal control based on reinforcement learning with graph convolutional neural nets. In: 2018 21st International conference on intelligent transportation systems (ITSC). IEEE, pp 877\u2013883","DOI":"10.1109\/ITSC.2018.8569301"},{"key":"2648_CR27","doi-asserted-by":"crossref","unstructured":"Zhang Y, Zhou Y, Lu H, Fujita H (2021) Spark cloud-based parallel computing for traffic network flow predictive control using non-analytical predictive model. IEEE Trans Intell Transp Syst","DOI":"10.1109\/TITS.2021.3071862"},{"key":"2648_CR28","doi-asserted-by":"crossref","unstructured":"Bi Z, Yu L, Gao H, Zhou P, Yao H (2020) Improved VGG model-based efficient traffic sign recognition for safe driving in 5G scenarios. Int J Mach Learn Cybern :1\u201312","DOI":"10.1007\/s13042-020-01185-5"},{"issue":"4","key":"2648_CR29","doi-asserted-by":"publisher","first-page":"1405","DOI":"10.1007\/s11036-019-01458-6","volume":"25","author":"L Kuang","year":"2020","unstructured":"Kuang L, Hua C, Wu J, Yin Y, Gao H (2020) Traffic volume prediction based on multi-sources GPS trajectory data by temporal convolutional network. Mobile Netw Appl 25(4):1405","journal-title":"Mobile Netw Appl"},{"key":"2648_CR30","doi-asserted-by":"crossref","unstructured":"Nagy AM, Simon V (2018) Survey on traffic prediction in smart cities. Pervasive Mob Comput : S1574119217306,521\u2013","DOI":"10.1016\/j.pmcj.2018.07.004"},{"key":"2648_CR31","unstructured":"Ahmed MS, Cook AR (1979) Analysis of freeway traffic time-series data by using Box-Jenkins techniques. 722"},{"issue":"1","key":"2648_CR32","doi-asserted-by":"publisher","first-page":"74","DOI":"10.3141\/1857-09","volume":"1857","author":"Y Kamarianakis","year":"2003","unstructured":"Kamarianakis Y, Prastacos P (2003) Forecasting traffic flow conditions in an urban network: Comparison of multivariate and univariate approaches. Transp Res Rec 1857(1):74","journal-title":"Transp Res Rec"},{"issue":"5","key":"2648_CR33","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1016\/S0968-090X(97)82903-8","volume":"4","author":"M Van Der Voort","year":"1996","unstructured":"Van Der Voort M, Dougherty M, Watson S (1996) Combining Kohonen maps with ARIMA time series models to forecast traffic flow. Transp Res C Emerg Technol 4(5):307","journal-title":"Transp Res C Emerg Technol"},{"issue":"4","key":"2648_CR34","doi-asserted-by":"publisher","first-page":"606","DOI":"10.1016\/j.trc.2010.10.002","volume":"19","author":"W Min","year":"2011","unstructured":"Min W, Wynter L (2011) Real-time road traffic prediction with spatio-temporal correlations. Transp Res C Emerg Technol 19(4):606","journal-title":"Transp Res C Emerg Technol"},{"issue":"6","key":"2648_CR35","doi-asserted-by":"publisher","first-page":"608","DOI":"10.1061\/(ASCE)0733-947X(2003)129:6(608)","volume":"129","author":"SIJ Chien","year":"2003","unstructured":"Chien SIJ, Kuchipudi CM (2003) Dynamic travel time prediction with real-time and historic data. J Transp Eng 129(6):608","journal-title":"J Transp Eng"},{"issue":"4","key":"2648_CR36","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1080\/15472450.2013.806844","volume":"18","author":"A Anand","year":"2014","unstructured":"Anand A, Ramadurai G, Vanajakshi L (2014) Data fusion-based traffic density estimation and prediction. J Intell Transp Syst 18(4):367","journal-title":"J Intell Transp Syst"},{"key":"2648_CR37","doi-asserted-by":"crossref","unstructured":"Nikovski D, Nishiuma N, Goto Y, Kumazawa H (2005) Univariate short-term prediction of road travel times. In: Proceedings. 2005 IEEE Intelligent transportation systems, 2005. IEEE, pp 1074\u20131079","DOI":"10.1109\/ITSC.2005.1520200"},{"key":"2648_CR38","doi-asserted-by":"crossref","unstructured":"Li S, Shen Z, Wang FY (2012) A weighted pattern recognition algorithm for short-term traffic flow forecasting. In: Proceedings of 2012 9th IEEE international conference on networking, sensing and control. IEEE, pp 1\u20136","DOI":"10.1109\/ICNSC.2012.6204881"},{"key":"2648_CR39","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, Yu H, Wang Y (2015) Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transp Res C Emerg Technol 54:187","journal-title":"Transp Res C Emerg Technol"},{"key":"2648_CR40","unstructured":"Chung J, Gulcehre C, Cho K, Bengio Y (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv:1412.3555"},{"key":"2648_CR41","unstructured":"Zhang J, Shi X, Xie J, Ma H, King I, Yeung D (2018) GaAN: Gated attention networks for learning on large and spatiotemporal graphs. arXiv:1803.07294"},{"key":"2648_CR42","doi-asserted-by":"publisher","unstructured":"Guo S, Lin Y, Feng N, Song C, Wan H (2019) Multi-range attentive bicomponent graph convolutional network for traffic forecasting. In: Proceedings of the AAAI conference on artificial intelligence. https:\/\/doi.org\/10.1609\/aaai.v33i01.3301922, vol 33, p 922","DOI":"10.1609\/aaai.v33i01.3301922"},{"key":"2648_CR43","doi-asserted-by":"crossref","unstructured":"Chen W, Chen L, Xie Y, Cao W, Gao Y, Feng X (2020) Multi-range attentive bicomponent graph convolutional network for traffic forecasting. In: Proceedings of the AAAI conference on artificial intelligence, vol 34, pp 3529\u20133536","DOI":"10.1609\/aaai.v34i04.5758"},{"key":"2648_CR44","doi-asserted-by":"crossref","unstructured":"Zheng C, Fan X, Wang C, Qi J (2020) GMAN: a graph multi-attention network for traffic prediction. In: AAAI, pp 1234\u2013 1241","DOI":"10.1609\/aaai.v34i01.5477"},{"key":"2648_CR45","doi-asserted-by":"crossref","unstructured":"Park C, Lee C, Bahng H, Tae Y, Jin S, Kim K, Ko S, Choo J (2020) ST-GRAT: a novel spatio-temporal graph attention networks for accurately forecasting dynamically changing road speed. In: Proceedings of the 29th ACM international conference on information & knowledge management, pp 1215\u20131224","DOI":"10.1145\/3340531.3411940"},{"key":"2648_CR46","unstructured":"Gori M, Monfardini G, Scarselli F (2005) A new model for learning in graph domains. In: IEEE International joint conference on neural networks"},{"issue":"1","key":"2648_CR47","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","volume":"20","author":"S Franco","year":"2009","unstructured":"Franco S, Marco G, Chung TAh, Markus H, Gabriele M (2009) The graph neural network model. IEEE Trans Neural Netw 20(1):61","journal-title":"IEEE Trans Neural Netw"},{"key":"2648_CR48","doi-asserted-by":"crossref","unstructured":"Wu Z, Pan S, Chen F, Long G, Zhang C, Philip SY (2020) A comprehensive survey on graph neural networks. IEEE Trans Neural Netw Learn Syst","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"2648_CR49","unstructured":"Bruna J, Zaremba W, Szlam A, LeCun Y (2013) Spectral networks and locally connected networks on graphs"},{"key":"2648_CR50","unstructured":"Defferrard M, Bresson X, Vandergheynst P (2016) Convolutional neural networks on graphs with fast localized spectral filtering. In: Lee DD, Sugiyama M, Luxburg UV, Guyon I, Garnett R (eds) Advances in neural information processing systems, vol 29. Curran associates Inc., pp 3844\u20133852"},{"key":"2648_CR51","unstructured":"Bahdanau D, Cho KH, Bengio Y (2015) Neural machine translation by jointly learning to align and translate. In: 3rd international conference on learning representations, ICLR 2015"},{"key":"2648_CR52","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141., Polosukhin I (2017) Attention is all you need. In: Advances in neural information processing systems, pp 5998\u20136008"},{"key":"2648_CR53","doi-asserted-by":"crossref","unstructured":"Iida S, Kimura R, Cui H, Hung PH, Utsuro T, Nagata M (2019) Attention over heads: A multi-hop attention for neural machine translation. In: Proceedings of the 57th annual meeting of the association for computational linguistics: student research workshop, pp 217\u2013222","DOI":"10.18653\/v1\/P19-2030"},{"issue":"3","key":"2648_CR54","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1109\/MSP.2012.2235192","volume":"30","author":"DI Shuman","year":"2013","unstructured":"Shuman DI, Narang SK, Frossard P, Ortega A, Vandergheynst P (2013) The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE Signal Process Mag 30(3):83","journal-title":"IEEE Signal Process Mag"},{"key":"2648_CR55","doi-asserted-by":"crossref","unstructured":"Grover A, Leskovec J (2016) node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, pp 855\u2013864","DOI":"10.1145\/2939672.2939754"},{"issue":"4","key":"2648_CR56","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1155\/S1024123X04403068","volume":"2004","author":"S Nadarajah","year":"2004","unstructured":"Nadarajah S, Kotz S (2004) The beta Gumbel distribution. Math Probl Eng 2004(4):323","journal-title":"Math Probl Eng"},{"key":"2648_CR57","doi-asserted-by":"publisher","first-page":"134363","DOI":"10.1109\/ACCESS.2020.3011186","volume":"8","author":"X Kong","year":"2020","unstructured":"Kong X, Xing W, Wei X, Bao P, Zhang J, Lu W (2020) STGAT: spatial-temporal graph attention networks for traffic flow forecasting. IEEE Access 8:134363","journal-title":"IEEE Access"},{"key":"2648_CR58","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431\u20133440","DOI":"10.1109\/CVPR.2015.7298965"},{"issue":"7","key":"2648_CR59","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1145\/2611567","volume":"57","author":"HV Jagadish","year":"2014","unstructured":"Jagadish HV, Gehrke J, Labrinidis A, Papakonstantinou Y, Patel JM, Ramakrishnan R, Shahabi C (2014) Big data and its technical challenges. Commun Acm 57(7):86","journal-title":"Commun Acm"},{"key":"2648_CR60","unstructured":"Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. Adv Neural Inf Process Syst :3104\u20133112"},{"key":"2648_CR61","doi-asserted-by":"crossref","unstructured":"Oreshkin BN, Amini A, Coyle L, Coates MJ (2021) FC-GAGA: Fully connected gated graph architecture for spatio-temporal traffic forecasting. In: AAAI","DOI":"10.1609\/aaai.v35i10.17114"},{"key":"2648_CR62","unstructured":"Kingma DP, Ba J (2015) Adam: A Method for Stochastic Optimization. In: ICLR (Poster)"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-02648-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-021-02648-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-02648-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,5]],"date-time":"2023-01-05T00:48:17Z","timestamp":1672879697000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-021-02648-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,20]]},"references-count":62,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2022,3]]}},"alternative-id":["2648"],"URL":"https:\/\/doi.org\/10.1007\/s10489-021-02648-0","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,20]]},"assertion":[{"value":"25 June 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 July 2021","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":"<!--Emphasis Type='Bold' removed-->Conflict of Interests"}}]}}