{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T14:37:43Z","timestamp":1777127863971,"version":"3.51.4"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2023,12,19]],"date-time":"2023-12-19T00:00:00Z","timestamp":1702944000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,12,19]],"date-time":"2023-12-19T00:00:00Z","timestamp":1702944000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62176088"],"award-info":[{"award-number":["62176088"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Science and Technology Research Project of Henan Province of China","award":["22102210067"],"award-info":[{"award-number":["22102210067"]}]},{"name":"Key Science and Technology Research Project of Henan Province of China","award":["222102210022"],"award-info":[{"award-number":["222102210022"]}]},{"name":"Program for Science & Technology Development of Henan Province","award":["212102210412"],"award-info":[{"award-number":["212102210412"]}]},{"name":"Program for Science & Technology Development of Henan Province","award":["202102310198"],"award-info":[{"award-number":["202102310198"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2024,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Various external factors that interfere with traffic flow, such as weather conditions, traffic accidents, incidents, and Points of Interest (POIs), need to be considered in performing traffic forecasting tasks. However, the current research methods encounter difficulties in effectively incorporating these factors with traffic characteristics and efficiently updating them, which leads to a lack of dynamics and interpretability. Moreover, capturing temporal dependence and spatial dependence separately and sequentially can result in issues, such as information loss and model errors. To address these challenges, we present a Knowledge Representation learning-actuated spatial\u2013temporal graph neural network (KR-STGNN) for traffic flow prediction. We combine the knowledge embedding with the traffic features via Gated Feature Fusion Module (GFFM), and dynamically update the traffic features adaptively according to the importance of external factors. To conduct the co-capture of spatial\u2013temporal dependencies, we subsequently propose a spatial\u2013temporal feature synchronous capture module (ST-FSCM) combining dilation causal convolution with GRU. Experimental results on a real-world traffic data set demonstrate that KR-STGNN has superior forecasting performances over diverse prediction horizons, especially for short-term prediction. The ablation and perturbation analysis experiments further validate the effectiveness and robustness of the designed method.<\/jats:p>","DOI":"10.1007\/s40747-023-01299-7","type":"journal-article","created":{"date-parts":[[2023,12,19]],"date-time":"2023-12-19T05:27:42Z","timestamp":1702963662000},"page":"2883-2900","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Integrating knowledge representation into traffic prediction: a spatial\u2013temporal graph neural network with adaptive fusion features"],"prefix":"10.1007","volume":"10","author":[{"given":"Yi","family":"Zhou","sequence":"first","affiliation":[]},{"given":"Yihan","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Nianwen","family":"Ning","sequence":"additional","affiliation":[]},{"given":"Li","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Zixing","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xiaozhi","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Ning","family":"Lu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,19]]},"reference":[{"issue":"4","key":"1299_CR1","doi-asserted-by":"publisher","first-page":"3922","DOI":"10.1109\/TITS.2022.3233801","volume":"24","author":"K Ramana","year":"2023","unstructured":"Ramana K, Srivastava G, Kumar MR, Gadekallu TR, Lin JC-W, Alazab M, Iwendi C (2023) A vision transformer approach for traffic congestion prediction in urban areas. IEEE Trans Intell Transp Syst 24(4):3922\u20133934. https:\/\/doi.org\/10.1109\/TITS.2022.3233801","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"1299_CR2","doi-asserted-by":"publisher","first-page":"117798","DOI":"10.1016\/j.eswa.2022.117798","volume":"207","author":"M Wo\u017aniak","year":"2022","unstructured":"Wo\u017aniak M, Zielonka A, Sikora A (2022) Driving support by type-2 fuzzy logic control model. Expert Syst Appl 207:117798. https:\/\/doi.org\/10.1016\/j.eswa.2022.117798","journal-title":"Expert Syst Appl"},{"key":"1299_CR3","doi-asserted-by":"publisher","first-page":"103921","DOI":"10.1016\/j.trc.2022.103921","volume":"145","author":"M Shaygan","year":"2022","unstructured":"Shaygan M, Meese C, Li W, Zhao XG, Nejad M (2022) Traffic prediction using artificial intelligence: review of recent advances and emerging opportunities. Transp Res Part C Emerg Technol 145:103921. https:\/\/doi.org\/10.1016\/j.trc.2022.103921","journal-title":"Transp Res Part C Emerg Technol"},{"key":"1299_CR4","doi-asserted-by":"publisher","unstructured":"Fu R, Zhang Z, Li L (2016) Using LSTM and GRU neural network methods for traffic flow prediction. In: 2016 31st Youth academic annual conference of Chinese association of automation (YAC), pp. 324\u2013328. https:\/\/doi.org\/10.1109\/YAC.2016.7804912","DOI":"10.1109\/YAC.2016.7804912"},{"issue":"9","key":"1299_CR5","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. https:\/\/doi.org\/10.1109\/TITS.2022.3195605","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"9","key":"1299_CR6","doi-asserted-by":"publisher","first-page":"16654","DOI":"10.1109\/TITS.2021.3094659","volume":"23","author":"W Shu","year":"2021","unstructured":"Shu W, Cai K, Xiong NN (2021) A short-term traffic flow prediction model based on an improved gate recurrent unit neural network. IEEE Trans Intell Transp Syst 23(9):16654\u201316665. https:\/\/doi.org\/10.1109\/TITS.2021.3094659","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"1299_CR7","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1016\/j.ins.2022.02.031","volume":"594","author":"F Huang","year":"2022","unstructured":"Huang F, Yi P, Wang J, Li M, Peng J, Xiong X (2022) A dynamical spatial-temporal graph neural network for traffic demand prediction. Inf Sci 594:286\u2013304. https:\/\/doi.org\/10.1016\/j.ins.2022.02.031","journal-title":"Inf Sci"},{"key":"1299_CR8","doi-asserted-by":"publisher","first-page":"4969","DOI":"10.1109\/TKDE.2022.3150080","volume":"35","author":"B Xue","year":"2023","unstructured":"Xue B, Zou L (2023) Knowledge graph quality management: a comprehensive survey. IEEE Trans Knowl Data Eng 35:4969\u20134988. https:\/\/doi.org\/10.1109\/TKDE.2022.3150080","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"4","key":"1299_CR9","doi-asserted-by":"publisher","first-page":"045406","DOI":"10.1088\/1361-6501\/acb075","volume":"34","author":"L Shen","year":"2023","unstructured":"Shen L, Tao H, Ni Y, Wang Y, Stojanovic V (2023) Improved yolov3 model with feature map cropping for multi-scale road object detection. Meas Sci Technol 34(4):045406. https:\/\/doi.org\/10.1088\/1361-6501\/acb075","journal-title":"Meas Sci Technol"},{"issue":"2","key":"1299_CR10","doi-asserted-by":"publisher","first-page":"3461","DOI":"10.1109\/TSMC.2022.3225381","volume":"53","author":"Z Zhuang","year":"2022","unstructured":"Zhuang Z, Tao H, Chen Y, Stojanovic V, Paszke W (2022) An optimal iterative learning control approach for linear systems with nonuniform trial lengths under input constraints. IEEE Trans Syst Man Cybern-Syst 53(2):3461\u20133473. https:\/\/doi.org\/10.1109\/TSMC.2022.3225381","journal-title":"IEEE Trans Syst Man Cybern-Syst"},{"issue":"2","key":"1299_CR11","doi-asserted-by":"publisher","first-page":"105513","DOI":"10.1016\/j.conengprac.2023.105513","volume":"135","author":"X Song","year":"2023","unstructured":"Song X, Wu C, Stojanovic V, Song S (2023) 1 bit encoding-decoding-based event-triggered fixed-time adaptive control for unmanned surface vehicle with guaranteed tracking performance. Control Eng Practice 135(2):105513. https:\/\/doi.org\/10.1016\/j.conengprac.2023.105513","journal-title":"Control Eng Practice"},{"issue":"2","key":"1299_CR12","doi-asserted-by":"publisher","first-page":"100159","DOI":"10.1016\/j.jnlest.2022.100159","volume":"20","author":"L Tian","year":"2022","unstructured":"Tian L, Zhou X, Wu Y-P, Zhou W-T, Zhang J-H, Zhang T-S (2022) Knowledge graph and knowledge reasoning: a systematic review. J Electron Sci Technol 20(2):100159. https:\/\/doi.org\/10.1016\/j.jnlest.2022.100159","journal-title":"J Electron Sci Technol"},{"issue":"6","key":"1299_CR13","doi-asserted-by":"publisher","first-page":"4927","DOI":"10.1109\/TITS.2021.3054840","volume":"23","author":"X Yin","year":"2021","unstructured":"Yin X, Wu G, Wei J, Shen Y, Qi H, Yin B (2021) Deep learning on traffic prediction: methods, analysis, and future directions. IEEE Trans Intell Transp Syst 23(6):4927\u20134943. https:\/\/doi.org\/10.1109\/TITS.2021.3054840","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"1299_CR14","doi-asserted-by":"publisher","first-page":"619","DOI":"10.1007\/s41870-021-00852-2","volume":"14","author":"N Nidhi","year":"2016","unstructured":"Nidhi N, Lobiyal D (2016) Traffic flow prediction using support vector regression. Int J Inf Technol 14:619\u2013626. https:\/\/doi.org\/10.1007\/s41870-021-00852-2","journal-title":"Int J Inf Technol"},{"issue":"1","key":"1299_CR15","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1109\/TITS.2006.869623","volume":"7","author":"S Sun","year":"2006","unstructured":"Sun S, Zhang C, Yu G (2006) A Bayesian network approach to traffic flow forecasting. IEEE Trans Intell Transp Syst 7(1):124\u2013132. https:\/\/doi.org\/10.1109\/TITS.2006.869623","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"1299_CR16","doi-asserted-by":"publisher","first-page":"107868","DOI":"10.1016\/j.ijpe.2020.107868","volume":"231","author":"S Kaffash","year":"2021","unstructured":"Kaffash S, Nguyen AT, Zhu J (2021) Big data algorithms and applications in intelligent transportation system: a review and bibliometric analysis. Int J Prod Econ 231:107868. https:\/\/doi.org\/10.1016\/j.ijpe.2020.107868","journal-title":"Int J Prod Econ"},{"key":"1299_CR17","doi-asserted-by":"publisher","first-page":"3827","DOI":"10.1109\/TSC.2023.3286332","volume":"16","author":"W Wei","year":"2023","unstructured":"Wei W, Ke Q, Zielonka A, Pleszczy\u0144ski M, Wo\u017aniak M (2023) Vehicle parking navigation based on edge computing with diffusion model and information potential field. IEEE Trans Serv Comput 16:3827\u20133836. https:\/\/doi.org\/10.1109\/TSC.2023.3286332","journal-title":"IEEE Trans Serv Comput"},{"issue":"5","key":"1299_CR18","doi-asserted-by":"publisher","first-page":"3015","DOI":"10.1109\/TNSE.2021.3126830","volume":"9","author":"Q Zhang","year":"2021","unstructured":"Zhang Q, Yu K, Guo Z, Garg S, Rodrigues JJ, Hassan MM, Guizani M (2021) Graph neural network-driven traffic forecasting for the connected internet of vehicles. IEEE Trans Netw Sci Eng 9(5):3015\u20133027. https:\/\/doi.org\/10.1109\/TNSE.2021.3126830","journal-title":"IEEE Trans Netw Sci Eng"},{"key":"1299_CR19","doi-asserted-by":"publisher","unstructured":"Yu B, Yin H, Zhu Z (2018) Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence, pp. 3634\u20133640. https:\/\/doi.org\/10.5555\/3304222.3304273","DOI":"10.5555\/3304222.3304273"},{"key":"1299_CR20","doi-asserted-by":"publisher","unstructured":"Wu Z, Pan S, Long G, Jiang J, Zhang C (2019) Graph wavenet for deep spatial-temporal graph modeling. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence, pp. 1907\u20131913. https:\/\/doi.org\/10.5555\/3367243.3367303","DOI":"10.5555\/3367243.3367303"},{"issue":"9","key":"1299_CR21","doi-asserted-by":"publisher","first-page":"3848","DOI":"10.1109\/TITS.2019.2935152","volume":"21","author":"L Zhao","year":"2019","unstructured":"Zhao L, Song Y, Zhang C, 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. https:\/\/doi.org\/10.1109\/TITS.2019.2935152","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"1299_CR22","doi-asserted-by":"publisher","unstructured":"Ge L, Li H, Liu J, Zhou A (2019) Temporal graph convolutional networks for traffic speed prediction considering external factors. In: 2019 20th IEEE International Conference on Mobile Data Management (MDM), pp. 234\u2013242. https:\/\/doi.org\/10.1109\/MDM.2019.00-52","DOI":"10.1109\/MDM.2019.00-52"},{"key":"1299_CR23","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1016\/j.neunet.2021.10.021","volume":"145","author":"A Ali","year":"2022","unstructured":"Ali A, Zhu Y, Zakarya M (2022) Exploiting dynamic spatio-temporal graph convolutional neural networks for citywide traffic flows prediction. Neural Netw 145:233\u2013247. https:\/\/doi.org\/10.1016\/j.neunet.2021.10.021","journal-title":"Neural Netw"},{"key":"1299_CR24","doi-asserted-by":"publisher","unstructured":"Xie Q, Guo T, Chen Y, Xiao Y, Wang X, Zhao BY (2019) \u2019how do urban incidents affect traffic speed?\u2019 a deep graph convolutional network for incident-driven traffic speed prediction. arXiv preprint, arXiv:1912.01242. https:\/\/doi.org\/10.48550\/arXiv.1912.01242","DOI":"10.48550\/arXiv.1912.01242"},{"issue":"12","key":"1299_CR25","doi-asserted-by":"publisher","first-page":"7642","DOI":"10.1109\/TITS.2020.3006227","volume":"22","author":"Z He","year":"2020","unstructured":"He Z, Chow C-Y, Zhang J-D (2020) Stnn: a spatio-temporal neural network for traffic predictions. IEEE Trans Intell Transp Syst 22(12):7642\u20137651. https:\/\/doi.org\/10.1109\/TITS.2020.3006227","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"5","key":"1299_CR26","doi-asserted-by":"publisher","first-page":"2348","DOI":"10.1109\/TKDE.2020.3008774","volume":"34","author":"J Sun","year":"2020","unstructured":"Sun J, Zhang J, Li Q, Yi X, Liang Y, Zheng Y (2020) Predicting citywide crowd flows in irregular regions using multi-view graph convolutional networks. IEEE Trans Knowl Data Eng 34(5):2348\u20132359. https:\/\/doi.org\/10.1109\/TKDE.2020.3008774","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1299_CR27","doi-asserted-by":"publisher","first-page":"14773","DOI":"10.1007\/s10489-021-02770-z","volume":"52","author":"X Huang","year":"2021","unstructured":"Huang X, Ye Y, Wang C, Yang X, Xiong L (2021) A multi-mode traffic flow prediction method with clustering based attention convolution LSTM. Appl Intell 52:14773\u201314786. https:\/\/doi.org\/10.1007\/s10489-021-02770-z","journal-title":"Appl Intell"},{"key":"1299_CR28","doi-asserted-by":"publisher","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. https:\/\/doi.org\/10.1109\/ACCESS.2021.3062114","journal-title":"IEEE Access"},{"key":"1299_CR29","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1016\/j.trc.2019.08.005","volume":"107","author":"S Hao","year":"2019","unstructured":"Hao S, Lee D-H, Zhao D (2019) Sequence to sequence learning with attention mechanism for short-term passenger flow prediction in largescale metro system. Transp Res Part C Emerg Technol 107:287\u2013300. https:\/\/doi.org\/10.1016\/j.trc.2019.08.005","journal-title":"Transp Res Part C Emerg Technol"},{"key":"1299_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2021\/9513170","volume":"2021","author":"F Hou","year":"2021","unstructured":"Hou F, Zhang Y, Fu X, Jiao L, Zheng W (2021) The prediction of multistep traffic flow based on AST-GCN-LSTM. J Adv Transp 2021:1\u201310. https:\/\/doi.org\/10.1155\/2021\/9513170","journal-title":"J Adv Transp"},{"key":"1299_CR31","doi-asserted-by":"publisher","first-page":"826","DOI":"10.1016\/j.matpr.2021.04.249","volume":"81","author":"S Narmadha","year":"2021","unstructured":"Narmadha S, Vijayakumar V (2021) Spatio-temporal vehicle traffic flow prediction using multivariate CNN and LSTM model. Mater Today Proc 81:826\u2013833. https:\/\/doi.org\/10.1016\/j.matpr.2021.04.249","journal-title":"Mater Today Proc"},{"issue":"8","key":"1299_CR32","doi-asserted-by":"publisher","first-page":"8687","DOI":"10.1109\/TITS.2022.3201879","volume":"24","author":"X Qi","year":"2023","unstructured":"Qi X, Mei G, Tu J, Xi N, Piccialli F (2023) A deep learning approach for long-term traffic flow prediction with multifactor fusion using spatiotemporal graph convolutional network. IEEE Trans Intell Transp Syst 24(8):8687\u20138700. https:\/\/doi.org\/10.1109\/TITS.2022.3201879","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"2","key":"1299_CR33","doi-asserted-by":"publisher","first-page":"1515","DOI":"10.1109\/TVT.2022.3209242","volume":"72","author":"W Long","year":"2023","unstructured":"Long W, Xiao Z, Wang D, Jiang H, Chen J, Li Y, Alazab M (2023) Unified spatial-temporal neighbor attention network for dynamic traffic prediction. IEEE Trans Veh Technol 72(2):1515\u20131529. https:\/\/doi.org\/10.1109\/TVT.2022.3209242","journal-title":"IEEE Trans Veh Technol"},{"issue":"9","key":"1299_CR34","doi-asserted-by":"publisher","first-page":"15055","DOI":"10.1109\/TITS.2021.3136287","volume":"23","author":"J Zhu","year":"2022","unstructured":"Zhu J, Han X, Deng H, Tao C, Zhao L, Wang P, Lin T, Li H (2022) KST-GCN: a knowledge-driven spatio-temporal graph convolutional network for traffic forecasting. IEEE Trans Intell Transp Syst 23(9):15055\u201315065. https:\/\/doi.org\/10.1109\/TITS.2021.3136287","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"1299_CR35","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/2348375","author":"S Wang","year":"2022","unstructured":"Wang S, Lv Y, Peng Y, Piao X, Zhang Y (2022) Metro traffic flow prediction via knowledge graph and spatiotemporal graph neural network. J Adv Transp. https:\/\/doi.org\/10.1155\/2022\/2348375","journal-title":"J Adv Transp"},{"issue":"6","key":"1299_CR36","doi-asserted-by":"publisher","first-page":"1306","DOI":"10.3390\/electronics12061306","volume":"12","author":"H Xiong","year":"2023","unstructured":"Xiong H, Shen G, Lan X, Yuan H, Kong X (2023) Hit-gcn: spatial-temporal graph convolutional network embedded with heterogeneous information of road network for traffic forecasting. Electronics 12(6):1306. https:\/\/doi.org\/10.3390\/electronics12061306","journal-title":"Electronics"},{"key":"1299_CR37","doi-asserted-by":"publisher","first-page":"127079","DOI":"10.1016\/j.physa.2022.127079","volume":"595","author":"J Xing","year":"2022","unstructured":"Xing J, Wu W, Cheng Q, Liu R (2022) Traffic state estimation of urban road networks by multi-source data fusion: review and new insights. Physica A 595:127079. https:\/\/doi.org\/10.1016\/j.physa.2022.127079","journal-title":"Physica A"},{"issue":"8","key":"1299_CR38","doi-asserted-by":"publisher","first-page":"3961","DOI":"10.1109\/TNNLS.2021.3055147","volume":"33","author":"Z Li","year":"2021","unstructured":"Li Z, Liu H, Zhang Z, Liu T, Xiong NN (2021) Learning knowledge graph embedding with heterogeneous relation attention networks. IEEE Trans Neural Netw Learn Syst 33(8):3961\u20133973. https:\/\/doi.org\/10.1109\/TNNLS.2021.3055147","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"1299_CR39","doi-asserted-by":"publisher","first-page":"75729","DOI":"10.1109\/ACCESS.2022.3191784","volume":"10","author":"Z Ye","year":"2022","unstructured":"Ye Z, Kumar YJ, Sing GO, Song F, Wang J (2022) A comprehensive survey of graph neural networks for knowledge graphs. IEEE Access 10:75729\u201375741. https:\/\/doi.org\/10.1109\/ACCESS.2022.3191784","journal-title":"IEEE Access"},{"key":"1299_CR40","doi-asserted-by":"publisher","unstructured":"Li X, Lyu M, Wang Z, Chen C-H, Zheng P (2021) Exploiting knowledge graphs in industrial products and services: a survey of key aspects, challenges, and future perspectives. Comput Ind 129:103449. https:\/\/doi.org\/10.1016\/j.compind.2021.103449","DOI":"10.1016\/j.compind.2021.103449"},{"issue":"1","key":"1299_CR41","doi-asserted-by":"publisher","first-page":"1059","DOI":"10.1007\/s40747-022-00806-6","volume":"9","author":"S Verma","year":"2023","unstructured":"Verma S, Bhatia R, Harit S, Batish S (2023) Scholarly knowledge graphs through structuring scholarly communication: a review. Complex Intell Syst 9(1):1059\u20131095. https:\/\/doi.org\/10.1007\/s40747-022-00806-6","journal-title":"Complex Intell Syst"},{"key":"1299_CR42","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1016\/j.neucom.2022.02.028","volume":"485","author":"Y Ru","year":"2022","unstructured":"Ru Y, Qiu X, Tan X, Chen B, Gao Y, Jin Y (2022) Sparse-attentive meta temporal point process for clinical decision support. Neurocomputing 485:114\u2013123. https:\/\/doi.org\/10.1016\/j.neucom.2022.02.028","journal-title":"Neurocomputing"},{"key":"1299_CR43","doi-asserted-by":"publisher","unstructured":"Lin Y, Liu Z, Sun M, Liu Y, Zhu X (2015) Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of the AAAI conference on artificial intelligence 29(1). https:\/\/doi.org\/10.5555\/2886521.2886624","DOI":"10.5555\/2886521.2886624"},{"key":"1299_CR44","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1016\/j.neucom.2020.07.137","volume":"427","author":"Z Li","year":"2021","unstructured":"Li Z, Liu H, Zhang Z, Liu T, Shu J (2021) Recalibration convolutional networks for learning interaction knowledge graph embedding. Neurocomputing 427:118\u2013130. https:\/\/doi.org\/10.1016\/j.neucom.2020.07.137","journal-title":"Neurocomputing"},{"key":"1299_CR45","doi-asserted-by":"publisher","first-page":"107310","DOI":"10.1016\/j.knosys.2021.107310","volume":"229","author":"X Huang","year":"2021","unstructured":"Huang X, Tang J, Tan Z, Zeng W, Wang J, Zhao X (2021) Knowledge graph embedding by relational and entity rotation. Knowl Based Syst 229:107310. https:\/\/doi.org\/10.1016\/j.knosys.2021.107310","journal-title":"Knowl Based Syst"},{"key":"1299_CR46","doi-asserted-by":"publisher","first-page":"110035","DOI":"10.1016\/j.asoc.2023.110035","volume":"135","author":"S Feng","year":"2023","unstructured":"Feng S, Zhao L, Shi H, Wang M, Shen S, Wang W (2023) One dimensional vggnet for high-dimensional data. Appl Soft Comput 135:110035. https:\/\/doi.org\/10.1016\/j.asoc.2023.110035","journal-title":"Appl Soft Comput"},{"issue":"3","key":"1299_CR47","doi-asserted-by":"publisher","first-page":"1765","DOI":"10.1109\/TNSE.2022.3152983","volume":"9","author":"Z Liu","year":"2022","unstructured":"Liu Z, Zhang R, Wang C, Xiao Z, Jiang H (2022) Spatial-temporal conv-sequence learning with accident encoding for traffic flow prediction. IEEE Trans Netw Sci Eng 9(3):1765\u20131775. https:\/\/doi.org\/10.1109\/TNSE.2022.3152983","journal-title":"IEEE Trans Netw Sci Eng"},{"issue":"2","key":"1299_CR48","doi-asserted-by":"publisher","first-page":"871","DOI":"10.1109\/TITS.2013.2247040","volume":"14","author":"M Lippi","year":"2013","unstructured":"Lippi M, Bertini M, Frasconi P (2013) Short-term traffic flow forecasting: an experimental comparison of time-series analysis and supervised learning. IEEE Trans Intell Transp Syst 14(2):871\u2013882. https:\/\/doi.org\/10.1109\/TITS.2013.2247040","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"1299_CR49","doi-asserted-by":"publisher","unstructured":"Li Y, Yu R, Shahabi C, Liu Y (2017) Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv preprint arXiv:1707.01926. https:\/\/doi.org\/10.48550\/arXiv.1707.01926","DOI":"10.48550\/arXiv.1707.01926"},{"key":"1299_CR50","doi-asserted-by":"publisher","unstructured":"Song C, Lin Y, Guo S, Wan H (2020) Spatial-temporal synchronous graph convolutional networks: a new framework for spatial-temporal network data forecasting. In: Proceedings of the AAAI conference on artificial intelligence vol. 34(1), pp. 914\u2013921. https:\/\/doi.org\/10.1609\/aaai.v34i01.5438","DOI":"10.1609\/aaai.v34i01.5438"},{"key":"1299_CR51","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. Transp Res Part C Emerg Technol 139:103659. https:\/\/doi.org\/10.1016\/j.trc.2022.103659","journal-title":"Transp Res Part C Emerg Technol"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-023-01299-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-023-01299-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-023-01299-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,30]],"date-time":"2024-03-30T15:35:57Z","timestamp":1711812957000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-023-01299-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,19]]},"references-count":51,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["1299"],"URL":"https:\/\/doi.org\/10.1007\/s40747-023-01299-7","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,19]]},"assertion":[{"value":"17 August 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 November 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 December 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Corresponding authors declare on behalf of all authors that there is no conflict of interest. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}