{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T10:06:57Z","timestamp":1775815617932,"version":"3.50.1"},"reference-count":61,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,11,15]],"date-time":"2024-11-15T00:00:00Z","timestamp":1731628800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,15]],"date-time":"2024-11-15T00:00:00Z","timestamp":1731628800000},"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":["Geoinformatica"],"published-print":{"date-parts":[[2025,7]]},"DOI":"10.1007\/s10707-024-00532-w","type":"journal-article","created":{"date-parts":[[2024,11,15]],"date-time":"2024-11-15T07:53:20Z","timestamp":1731657200000},"page":"377-401","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["MST-GNN: graph neural network with multi-granularity in space and time for traffic prediction"],"prefix":"10.1007","volume":"29","author":[{"given":"Xinru","family":"Zhao","sequence":"first","affiliation":[]},{"given":"Wenhao","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Yifan","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,15]]},"reference":[{"key":"532_CR1","doi-asserted-by":"publisher","first-page":"1624","DOI":"10.1109\/TITS.2011.2158001","volume":"12","author":"J Zhang","year":"2011","unstructured":"Zhang J, Wang F-Y, Wang K, Lin W-H, Xu X, Chen C (2011) Data-Driven Intelligent Transportation Systems: A Survey. IEEE Trans Intell Transp Syst 12:1624\u20131639. https:\/\/doi.org\/10.1109\/TITS.2011.2158001","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"532_CR2","unstructured":"Ahmed MS, Cook AR (1979) Analysis of freeway traffic time-series data by using box-jenkins techniques. Transp Res Rec"},{"key":"532_CR3","unstructured":"Stephanedes YJ, Michalopoulos PG, Plum RA (1980) Improved estimation of traffic flow for real time control. Transp Res Rec J Transp Res Board 28\u201339"},{"key":"532_CR4","doi-asserted-by":"publisher","first-page":"6164","DOI":"10.1016\/j.eswa.2008.07.069","volume":"36","author":"M Castro-Neto","year":"2009","unstructured":"Castro-Neto M, Jeong Y-S, Jeong M-K, Han LD (2009) Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions. Expert Syst Appl 36:6164\u20136173. https:\/\/doi.org\/10.1016\/j.eswa.2008.07.069","journal-title":"Expert Syst Appl"},{"key":"532_CR5","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1061\/(ASCE)0733-947X(1991)117:2(178)","volume":"117","author":"GA Davis","year":"1991","unstructured":"Davis GA, Nihan NL (1991) Nonparametric Regression and Short-Term Freeway Traffic Forecasting. J Transp Eng 117:178\u2013188. https:\/\/doi.org\/10.1061\/(ASCE)0733-947X(1991)117:2(178)","journal-title":"J Transp Eng"},{"key":"532_CR6","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1007\/s11063-015-9409-6","volume":"43","author":"W Hu","year":"2016","unstructured":"Hu W, Yan L, Liu K, Wang H (2016) A Short-term Traffic Flow Forecasting Method Based on the Hybrid PSO-SVR. Neural Process Lett 43:155\u2013172. https:\/\/doi.org\/10.1007\/s11063-015-9409-6","journal-title":"Neural Process Lett"},{"key":"532_CR7","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1080\/21680566.2020.1732247","volume":"8","author":"L Zheng","year":"2020","unstructured":"Zheng L, Huang H, Zhu C, Zhang K (2020) A tensor-based K-nearest neighbors method for traffic speed prediction under data missing. Transp B Transp Dyn 8:182\u2013199. https:\/\/doi.org\/10.1080\/21680566.2020.1732247","journal-title":"Transp B Transp Dyn"},{"key":"532_CR8","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1007\/s42486-020-00039-x","volume":"2","author":"X Fan","year":"2020","unstructured":"Fan X, Xiang C, Gong L, He X, Qu Y, Amirgholipour S, Xi Y, Nanda P, He X (2020) Deep learning for intelligent traffic sensing and prediction: recent advances and future challenges. CCF Trans Pervasive Comput Interact 2:240\u2013260. https:\/\/doi.org\/10.1007\/s42486-020-00039-x","journal-title":"CCF Trans Pervasive Comput Interact"},{"key":"532_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.117921","volume":"207","author":"W Jiang","year":"2022","unstructured":"Jiang W, Luo J (2022) Graph neural network for traffic forecasting: A survey. Expert Syst Appl 207:117921. https:\/\/doi.org\/10.1016\/j.eswa.2022.117921","journal-title":"Expert Syst Appl"},{"key":"532_CR10","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","volume":"32","author":"Z Wu","year":"2020","unstructured":"Wu Z, Pan S, Chen F, Long G, Zhang C, Yu PS (2020) A Comprehensive Survey on Graph Neural Networks. IEEE Trans Neural Netw Learn Syst 32:4\u201324. https:\/\/doi.org\/10.1109\/TNNLS.2020.2978386","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"532_CR11","unstructured":"Wu Y, Tan H (2016) Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework"},{"key":"532_CR12","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 21:3848\u20133858. https:\/\/doi.org\/10.1109\/TITS.2019.2935152","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"532_CR13","unstructured":"Henaff M, Bruna J, LeCun Y (2015) Deep convolutional networks on graph-structured data"},{"key":"532_CR14","doi-asserted-by":"crossref","unstructured":"Agafonov A (2020) Traffic Flow Prediction Using Graph Convolution Neural Networks. In: 2020 10th International Conference on Information Science and Technology (ICIST). pp 91\u201395","DOI":"10.1109\/ICIST49303.2020.9201971"},{"key":"532_CR15","doi-asserted-by":"publisher","first-page":"3529","DOI":"10.1609\/aaai.v34i04.5758","volume":"34","author":"W Chen","year":"2020","unstructured":"Chen W, Chen L, Xie Y, Cao W, Gao Y, Feng X (2020) Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecasting. Proc AAAI Conf Artif Intell 34:3529\u20133536. https:\/\/doi.org\/10.1609\/aaai.v34i04.5758","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"532_CR16","doi-asserted-by":"publisher","first-page":"1009","DOI":"10.1109\/TITS.2020.3019497","volume":"23","author":"K Guo","year":"2022","unstructured":"Guo K, Hu Y, Qian Z, Sun Y, Gao J, Yin B (2022) Dynamic Graph Convolution Network for Traffic Forecasting Based on Latent Network of Laplace Matrix Estimation. IEEE Trans Intell Transp Syst 23:1009\u20131018. https:\/\/doi.org\/10.1109\/TITS.2020.3019497","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"1","key":"532_CR17","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1080\/13658816.2019.1650936","volume":"34","author":"W Yu","year":"2020","unstructured":"Yu W, Zhang Y, Ai T, Guan Q, Chen Z, Li H (2020) Road network generalization considering traffic flow patterns. Int J Geogr Inf Sci 34(1):119\u2013149","journal-title":"Int J Geogr Inf Sci"},{"key":"532_CR18","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.trc.2019.09.008","volume":"108","author":"LNN Do","year":"2019","unstructured":"Do LNN, Vu HL, Vo BQ, Liu Z, Phung D (2019) An effective spatial-temporal attention based neural network for traffic flow prediction. Transp Res Part C Emerg Technol 108:12\u201328. https:\/\/doi.org\/10.1016\/j.trc.2019.09.008","journal-title":"Transp Res Part C Emerg Technol"},{"key":"532_CR19","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. Proc AAAI Conf Artif Intell 33:922\u2013929. https:\/\/doi.org\/10.1609\/aaai.v33i01.3301922","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"532_CR20","doi-asserted-by":"publisher","first-page":"4909","DOI":"10.1109\/TITS.2020.2983651","volume":"22","author":"X Shi","year":"2021","unstructured":"Shi X, Qi H, Shen Y, Wu G, Yin B (2021) A Spatial-Temporal Attention Approach for Traffic Prediction. IEEE Trans Intell Transp Syst 22:4909\u20134918. https:\/\/doi.org\/10.1109\/TITS.2020.2983651","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"532_CR21","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1016\/j.patrec.2022.03.005","volume":"156","author":"J Su","year":"2022","unstructured":"Su J, Jin Z, Ren J, Yang J, Liu Y (2022) GDFormer: A Graph Diffusing Attention based approach for Traffic Flow Prediction. Pattern Recognit Lett 156:126\u2013132. https:\/\/doi.org\/10.1016\/j.patrec.2022.03.005","journal-title":"Pattern Recognit Lett"},{"key":"532_CR22","doi-asserted-by":"publisher","first-page":"1234","DOI":"10.1609\/aaai.v34i01.5477","volume":"34","author":"C Zheng","year":"2020","unstructured":"Zheng C, Fan X, Wang C, Qi J (2020) GMAN: A Graph Multi-Attention Network for Traffic Prediction. Proc AAAI Conf Artif Intell 34:1234\u20131241. https:\/\/doi.org\/10.1609\/aaai.v34i01.5477","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"532_CR23","doi-asserted-by":"crossref","unstructured":"Chai D, Wang L, Yang Q (2018) Bike flow prediction with multi-graph convolutional networks. In: Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. Association for Computing Machinery, New York, NY, USA, pp 397\u2013400","DOI":"10.1145\/3274895.3274896"},{"key":"532_CR24","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.neucom.2022.09.010","volume":"510","author":"G Jin","year":"2022","unstructured":"Jin G, Xi Z, Sha H, Feng Y, Huang J (2022) Deep multi-view graph-based network for citywide ride-hailing demand prediction. Neurocomputing 510:79\u201394. https:\/\/doi.org\/10.1016\/j.neucom.2022.09.010","journal-title":"Neurocomputing"},{"key":"532_CR25","doi-asserted-by":"crossref","unstructured":"Zhang Y, Yu W, Zhu D (2024) Next track point prediction using a flexible strategy of subgraph learning on road networks.\u00a0Int J Geogr Inf Sci 1\u201326","DOI":"10.1080\/13658816.2024.2358527"},{"key":"532_CR26","doi-asserted-by":"publisher","first-page":"3656","DOI":"10.1609\/aaai.v33i01.33013656","volume":"33","author":"X Geng","year":"2019","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. Proc AAAI Conf Artif Intell 33:3656\u20133663. https:\/\/doi.org\/10.1609\/aaai.v33i01.33013656","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"532_CR27","doi-asserted-by":"publisher","first-page":"5388","DOI":"10.1109\/TKDE.2023.3333824","volume":"36","author":"G Jin","year":"2024","unstructured":"Jin G, Liang Y, Fang Y, Shao Z, Huang J, Zhang J, Zheng Y (2024) Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey. IEEE Trans Knowl Data Eng 36:5388\u20135408. https:\/\/doi.org\/10.1109\/TKDE.2023.3333824","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"532_CR28","doi-asserted-by":"publisher","first-page":"2348","DOI":"10.1109\/TKDE.2020.3008774","volume":"34","author":"J Sun","year":"2022","unstructured":"Sun J, Zhang J, Li Q, Yi X, Liang Y, Zheng Y (2022) Predicting Citywide Crowd Flows in Irregular Regions Using Multi-View Graph Convolutional Networks. IEEE Trans Knowl Data Eng 34:2348\u20132359. https:\/\/doi.org\/10.1109\/TKDE.2020.3008774","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"532_CR29","doi-asserted-by":"publisher","first-page":"15008","DOI":"10.1609\/aaai.v35i17.17761","volume":"35","author":"X Zhang","year":"2021","unstructured":"Zhang X, Huang C, Xu Y, Xia L, Dai P, Bo L, Zhang J, Zheng Y (2021) Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network. Proc AAAI Conf Artif Intell 35:15008\u201315015. https:\/\/doi.org\/10.1609\/aaai.v35i17.17761","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"532_CR30","doi-asserted-by":"crossref","unstructured":"He S, Shin KG (2020) Towards fine-grained flow forecasting: a graph attention approach for bike sharing systems. In: Proceedings of The Web Conference 2020. Association for Computing Machinery, New York, NY, USA, pp 88\u201398","DOI":"10.1145\/3366423.3380097"},{"key":"532_CR31","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 52:16104\u201316116. https:\/\/doi.org\/10.1007\/s10489-021-03022-w","journal-title":"Appl Intell"},{"key":"532_CR32","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1016\/j.neucom.2021.11.006","volume":"471","author":"Y Wang","year":"2022","unstructured":"Wang Y, Fang S, Zhang C, Xiang S, Pan C (2022) TVGCN: Time-variant graph convolutional network for traffic forecasting. Neurocomputing 471:118\u2013129. https:\/\/doi.org\/10.1016\/j.neucom.2021.11.006","journal-title":"Neurocomputing"},{"key":"532_CR33","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1609\/aaai.v35i1.16088","volume":"35","author":"K Guo","year":"2021","unstructured":"Guo K, Hu Y, Sun Y, Qian S, Gao J, Yin B (2021) Hierarchical Graph Convolution Network for Traffic Forecasting. Proc AAAI Conf Artif Intell 35:151\u2013159. https:\/\/doi.org\/10.1609\/aaai.v35i1.16088","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"532_CR34","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1177\/0361198120930010","volume":"2674","author":"T Mallick","year":"2020","unstructured":"Mallick T, Balaprakash P, Rask E, Macfarlane J (2020) Graph-Partitioning-Based Diffusion Convolutional Recurrent Neural Network for Large-Scale Traffic Forecasting. Transp Res Rec 2674:473\u2013488. https:\/\/doi.org\/10.1177\/0361198120930010","journal-title":"Transp Res Rec"},{"key":"532_CR35","doi-asserted-by":"publisher","first-page":"435","DOI":"10.1007\/978-3-030-59410-7_30","volume-title":"Database Systems for Advanced Applications","author":"F Wang","year":"2020","unstructured":"Wang F, Xu J, Liu C, Zhou R, Zhao P (2020) MTGCN: A Multitask Deep Learning Model for Traffic Flow Prediction. In: Nah Y, Cui B, Lee S-W, Yu JX, Moon Y-S, Whang SE (eds) Database Systems for Advanced Applications. Springer International Publishing, Cham, pp 435\u2013451"},{"key":"532_CR36","doi-asserted-by":"publisher","first-page":"1281","DOI":"10.1007\/s11067-019-09466-5","volume":"19","author":"R Ding","year":"2019","unstructured":"Ding R, Ujang N, Hamid HB, Manan MSA, Li R, Albadareen SSM, Nochian A, Wu J (2019) Application of Complex Networks Theory in Urban Traffic Network Researches. Netw Spat Econ 19:1281\u20131317. https:\/\/doi.org\/10.1007\/s11067-019-09466-5","journal-title":"Netw Spat Econ"},{"key":"532_CR37","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1016\/j.physa.2016.03.036","volume":"457","author":"L Sun","year":"2016","unstructured":"Sun L, Ling X, He K, Tan Q (2016) Community structure in traffic zones based on travel demand. Phys Stat Mech Its Appl 457:356\u2013363. https:\/\/doi.org\/10.1016\/j.physa.2016.03.036","journal-title":"Phys Stat Mech Its Appl"},{"key":"532_CR38","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/BF00131538","volume":"3","author":"RV O\u2019Neill","year":"1989","unstructured":"O\u2019Neill RV, Johnson AR, King AW (1989) A hierarchical framework for the analysis of scale. Landsc Ecol 3:193\u2013205. https:\/\/doi.org\/10.1007\/BF00131538","journal-title":"Landsc Ecol"},{"key":"532_CR39","doi-asserted-by":"crossref","unstructured":"Yu B, Yin H, Zhu Z (2018) Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence. pp 3634\u20133640","DOI":"10.24963\/ijcai.2018\/505"},{"key":"532_CR40","doi-asserted-by":"publisher","first-page":"4189","DOI":"10.1609\/aaai.v35i5.16542","volume":"35","author":"M Li","year":"2021","unstructured":"Li M, Zhu Z (2021) Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting. Proc AAAI Conf Artif Intell 35:4189\u20134196. https:\/\/doi.org\/10.1609\/aaai.v35i5.16542","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"532_CR41","doi-asserted-by":"publisher","first-page":"6367","DOI":"10.1609\/aaai.v36i6.20587","volume":"36","author":"J Choi","year":"2022","unstructured":"Choi J, Choi H, Hwang J, Park N (2022) Graph Neural Controlled Differential Equations for Traffic Forecasting. Proc AAAI Conf Artif Intell 36:6367\u20136374. https:\/\/doi.org\/10.1609\/aaai.v36i6.20587","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"532_CR42","doi-asserted-by":"crossref","unstructured":"Li F, Yan H, Jin G, Liu Y, Li Y, Jin D (2022) Automated spatio-temporal synchronous modeling with multiple graphs for traffic prediction. In: Proceedings of the 31st ACM International Conference on Information & Knowledge Management. Association for Computing Machinery, New York, NY, USA, pp 1084\u20131093","DOI":"10.1145\/3511808.3557243"},{"key":"532_CR43","doi-asserted-by":"publisher","first-page":"8820","DOI":"10.1109\/TITS.2022.3195232","volume":"24","author":"G Jin","year":"2023","unstructured":"Jin G, Li F, Zhang J, Wang M, Huang J (2023) Automated Dilated Spatio-Temporal Synchronous Graph Modeling for Traffic Prediction. IEEE Trans Intell Transp Syst 24:8820\u20138830. https:\/\/doi.org\/10.1109\/TITS.2022.3195232","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"532_CR44","doi-asserted-by":"publisher","unstructured":"Li F, Feng J, Yan H, Jin G, Yang F, Sun F, Jin D, Li Y (2023) Dynamic graph convolutional recurrent network for traffic prediction: benchmark and solution. ACM Trans Knowl Discov Data 17:9:1\u20139:21. https:\/\/doi.org\/10.1145\/3532611","DOI":"10.1145\/3532611"},{"key":"532_CR45","doi-asserted-by":"publisher","first-page":"14268","DOI":"10.1609\/aaai.v37i12.26669","volume":"37","author":"G Jin","year":"2023","unstructured":"Jin G, Liu L, Li F, Huang J (2023) Spatio-Temporal Graph Neural Point Process for Traffic Congestion Event Prediction. Proc AAAI Conf Artif Intell 37:14268\u201314276. https:\/\/doi.org\/10.1609\/aaai.v37i12.26669","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"532_CR46","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1007\/s41095-022-0271-y","volume":"8","author":"M-H Guo","year":"2022","unstructured":"Guo M-H, Xu T-X, Liu J-J, Liu Z-N, Jiang P-T, Mu T-J, Zhang S-H, Martin RR, Cheng M-M, Hu S-M (2022) Attention mechanisms in computer vision: A survey. Comput Vis Media 8:331\u2013368. https:\/\/doi.org\/10.1007\/s41095-022-0271-y","journal-title":"Comput Vis Media"},{"key":"532_CR47","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.neucom.2021.03.091","volume":"452","author":"Z Niu","year":"2021","unstructured":"Niu Z, Zhong G, Yu H (2021) A review on the attention mechanism of deep learning. Neurocomputing 452:48\u201362. https:\/\/doi.org\/10.1016\/j.neucom.2021.03.091","journal-title":"Neurocomputing"},{"key":"532_CR48","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. Curran Associates, Inc."},{"key":"532_CR49","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.70.066111","volume":"70","author":"A Clauset","year":"2004","unstructured":"Clauset A, Newman MEJ, Moore C (2004) Finding community structure in very large networks. Phys Rev E 70:066111. https:\/\/doi.org\/10.1103\/PhysRevE.70.066111","journal-title":"Phys Rev E"},{"key":"532_CR50","doi-asserted-by":"crossref","unstructured":"Pons P, Latapy M (2005) Computing communities in large networks using random walks. In: Yolum pInar, G\u00fcng\u00f6r T, G\u00fcrgen F, \u00d6zturan C (eds) Computer and Information Sciences - ISCIS 2005. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 284\u2013293","DOI":"10.1007\/11569596_31"},{"key":"532_CR51","doi-asserted-by":"crossref","unstructured":"Wakita K, Tsurumi T (2007) Finding community structure in mega-scale social networks: [extended abstract]. In: Proceedings of the 16th international conference on World Wide Web. Association for Computing Machinery, New York, NY, USA, pp 1275\u20131276","DOI":"10.1145\/1242572.1242805"},{"key":"532_CR52","doi-asserted-by":"publisher","first-page":"P10008","DOI":"10.1088\/1742-5468\/2008\/10\/P10008","volume":"2008","author":"VD Blondel","year":"2008","unstructured":"Blondel VD, Guillaume J-L, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech Theory Exp 2008:P10008. https:\/\/doi.org\/10.1088\/1742-5468\/2008\/10\/P10008","journal-title":"J Stat Mech Theory Exp"},{"key":"532_CR53","doi-asserted-by":"crossref","unstructured":"Li S, Luo J, Xu J (2022) Study on regional traffic sub-area division based on improved Louvain algorithm and correlation degree. In: 2022 China Automation Congress (CAC). pp 3522\u20133527","DOI":"10.1109\/CAC57257.2022.10054867"},{"key":"532_CR54","doi-asserted-by":"publisher","first-page":"227","DOI":"10.3390\/ijgi10040227","volume":"10","author":"Y Zhang","year":"2021","unstructured":"Zhang Y, Zheng X, Chen M, Li Y, Yan Y, Wang P (2021) Urban Fine-Grained Spatial Structure Detection Based on a New Traffic Flow Interaction Analysis Framework. ISPRS Int J Geo-Inf 10:227. https:\/\/doi.org\/10.3390\/ijgi10040227","journal-title":"ISPRS Int J Geo-Inf"},{"key":"532_CR55","unstructured":"Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks"},{"key":"532_CR56","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 Part C Emerg Technol 54:187\u2013197. https:\/\/doi.org\/10.1016\/j.trc.2015.03.014","journal-title":"Transp Res Part C Emerg Technol"},{"key":"532_CR57","unstructured":"Jia Z, Chen C, Coifman B, Varaiya P (2001) The PeMS algorithms for accurate, real-time estimates of g-factors and speeds from single-loop detectors. In: ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585). pp 536\u2013541"},{"key":"532_CR58","doi-asserted-by":"crossref","unstructured":"Wu Z, Pan S, Long G, Jiang J, Zhang C (2019) Graph WaveNet for deep spatial-temporal graph modeling","DOI":"10.24963\/ijcai.2019\/264"},{"key":"532_CR59","unstructured":"Li Y, Yu R, Shahabi C, Liu Y (2018) Diffusion convolutional recurrent neural network: data-driven traffic forecasting"},{"key":"532_CR60","doi-asserted-by":"publisher","unstructured":"Wang S, Zhang M, Miao H, Peng Z, Yu PS (2022) Multivariate correlation-aware spatio-temporal graph convolutional networks for multi-scale traffic prediction. ACM Trans Intell Syst Technol 13:38:1\u201338:22. https:\/\/doi.org\/10.1145\/3469087","DOI":"10.1145\/3469087"},{"key":"532_CR61","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.124648","volume":"255","author":"H Li","year":"2024","unstructured":"Li H, Liu J, Han S, Zhou J, Zhang T, Philip Chen CL (2024) STFGCN: Spatial\u2013temporal fusion graph convolutional network for traffic prediction. Expert Syst Appl 255:124648. https:\/\/doi.org\/10.1016\/j.eswa.2024.124648","journal-title":"Expert Syst Appl"}],"container-title":["GeoInformatica"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10707-024-00532-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10707-024-00532-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10707-024-00532-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T00:44:19Z","timestamp":1757119459000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10707-024-00532-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,15]]},"references-count":61,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,7]]}},"alternative-id":["532"],"URL":"https:\/\/doi.org\/10.1007\/s10707-024-00532-w","relation":{},"ISSN":["1384-6175","1573-7624"],"issn-type":[{"value":"1384-6175","type":"print"},{"value":"1573-7624","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,15]]},"assertion":[{"value":"13 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 September 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 October 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 November 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declaration"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}