{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T21:03:02Z","timestamp":1767646982885,"version":"3.48.0"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T00:00:00Z","timestamp":1763942400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T00:00:00Z","timestamp":1763942400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100007538","name":"Civil Aviation Administration of China","doi-asserted-by":"publisher","award":["MHAQ2022032"],"award-info":[{"award-number":["MHAQ2022032"]}],"id":[{"id":"10.13039\/501100007538","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007538","name":"Civil Aviation Administration of China","doi-asserted-by":"publisher","award":["MHJY2022015"],"award-info":[{"award-number":["MHJY2022015"]}],"id":[{"id":"10.13039\/501100007538","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J. King Saud Univ. Comput. Inf. Sci."],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Accurate Air Traffic Flow Prediction (ATFP) is critical for enhancing airspace safety and efficiency. Achieving high-precision ATFP requires simultaneous consideration of traffic dynamics recurrence and spatiotemporal interaction. However, modeling both dimensions concurrently is challenging, as it demands that the model account for multiple latent factors. To address this, a novel deep learning-based model is proposed that simultaneously accounts for these two dimensions. First, key features with spatial representation value are distilled from the dual perspectives of causal influence and temporal correlation. Second, a sparse slope attention mechanism is designed to capture similar dynamic patterns in historical traffic flows. Finally, multi-scale temporal features are extracted from traffic flows, and a nonlinear activation function is employed to generate dynamic calibration factors, thereby reducing prediction biases across different date distributions. Experimental results on real-world air traffic datasets demonstrate that the proposed approach not only achieves superior performance in the ATFP task but also validates the effectiveness of each technical module in enhancing overall predictive accuracy. We believe the proposed model can serve as a valuable tool for airspace management, providing reliable decision support for air traffic controllers.<\/jats:p>","DOI":"10.1007\/s44443-025-00287-w","type":"journal-article","created":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T10:46:21Z","timestamp":1763981181000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Considering traffic dynamics recurrence and spatiotemporal interaction for air traffic flow prediction"],"prefix":"10.1007","volume":"37","author":[{"given":"Bo","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-1510-3914","authenticated-orcid":false,"given":"Weizhen","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhousheng","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,11,24]]},"reference":[{"key":"287_CR1","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1186\/s40537-021-00444-8","volume":"8","author":"L Alzubaidi","year":"2021","unstructured":"Alzubaidi L, Zhang J, Humaidi AJ, Al-Dujaili A, Duan Y, Al-Shamma O, Santamar\u00eda J, Fadhel MA, Al-Amidie M, Farhan L (2021) Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data 8:53. https:\/\/doi.org\/10.1186\/s40537-021-00444-8","journal-title":"J Big Data"},{"key":"287_CR2","doi-asserted-by":"publisher","first-page":"7471","DOI":"10.1007\/s00500-018-03683-9","volume":"23","author":"S Aslan","year":"2019","unstructured":"Aslan S (2019) Time-based information sharing approach for employed foragers of artificial bee colony algorithm. Soft Comput 23:7471\u20137494. https:\/\/doi.org\/10.1007\/s00500-018-03683-9","journal-title":"Soft Comput"},{"key":"287_CR3","doi-asserted-by":"publisher","first-page":"104789","DOI":"10.1016\/j.engappai.2022.104789","volume":"112","author":"S Aslan","year":"2022","unstructured":"Aslan S (2022) An immune plasma algorithm with a modified treatment schema for UCAV path planning. Eng Appl Artif Intell 112:104789. https:\/\/doi.org\/10.1016\/j.engappai.2022.104789","journal-title":"Eng Appl Artif Intell"},{"key":"287_CR4","doi-asserted-by":"publisher","first-page":"100468","DOI":"10.1016\/j.eij.2024.100468","volume":"26","author":"S Aslan","year":"2024","unstructured":"Aslan S, Demirci S (2024) An immune plasma algorithm with Q-learning based pandemic management for path planning of unmanned aerial vehicles. Egypt Inform J 26:100468. https:\/\/doi.org\/10.1016\/j.eij.2024.100468","journal-title":"Egypt Inform J"},{"key":"287_CR5","doi-asserted-by":"publisher","first-page":"101829","DOI":"10.1016\/j.aei.2022.101829","volume":"55","author":"S Aslan","year":"2023","unstructured":"Aslan S, Erkin T (2023a) A multi-population immune plasma algorithm for path planning of unmanned combat aerial vehicle. Adv Eng Inform 55:101829. https:\/\/doi.org\/10.1016\/j.aei.2022.101829","journal-title":"Adv Eng Inform"},{"key":"287_CR6","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1016\/j.jksuci.2022.06.004","volume":"35","author":"S Aslan","year":"2023","unstructured":"Aslan S, Erkin T (2023b) An immune plasma algorithm based approach for UCAV path planning. J King Saud Univ - Comput Inf Sci 35:56\u201369. https:\/\/doi.org\/10.1016\/j.jksuci.2022.06.004","journal-title":"J King Saud Univ - Comput Inf Sci"},{"key":"287_CR7","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1007\/s10586-024-04941-2","volume":"28","author":"S Aslan","year":"2025","unstructured":"Aslan S, Erkin T (2025b) DUALIPA: a new immune plasma algorithm for path planning of unmanned aerial vehicles. Cluster Comput 28:259. https:\/\/doi.org\/10.1007\/s10586-024-04941-2","journal-title":"Cluster Comput"},{"key":"287_CR8","doi-asserted-by":"publisher","first-page":"765","DOI":"10.1142\/S2301385025500475","volume":"13","author":"S Aslan","year":"2025","unstructured":"Aslan S, Ozkok FO (2025) Best individual guided immune plasma algorithm on solving path planning problem of unmanned combat aerial vehicles. Un Sys 13:765\u2013787. https:\/\/doi.org\/10.1142\/S2301385025500475","journal-title":"Un Sys"},{"key":"287_CR9","doi-asserted-by":"publisher","unstructured":"Aslan S, Erkin T (2025a) A multi-threaded back-and-forth algorithm for planning unmanned aerial vehicles. Aeronaut J 1\u201326. https:\/\/doi.org\/10.1017\/aer.2025.10037","DOI":"10.1017\/aer.2025.10037"},{"key":"287_CR10","doi-asserted-by":"publisher","unstructured":"Chen C, Chen X, Yang Y, Hang P (2023) Sparse attention graph convolution network for vehicle trajectory prediction. https:\/\/doi.org\/10.36227\/techrxiv.170292983.33574164\/v1","DOI":"10.36227\/techrxiv.170292983.33574164\/v1"},{"key":"287_CR11","doi-asserted-by":"publisher","first-page":"103869","DOI":"10.1016\/j.trc.2022.103869","volume":"144","author":"R Dalmau","year":"2022","unstructured":"Dalmau R (2022) Predicting the likelihood of airspace user rerouting to mitigate air traffic flow management delay. Transp Res C: Emerg Technol 144:103869. https:\/\/doi.org\/10.1016\/j.trc.2022.103869","journal-title":"Transp Res C: Emerg Technol"},{"key":"287_CR12","doi-asserted-by":"publisher","unstructured":"Dey R, Salem FM (2017) Gate-variants of Gated Recurrent Unit (GRU) neural networks. In: 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS). Presented at the 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS). IEEE, Boston, MA, pp 1597\u20131600. https:\/\/doi.org\/10.1109\/MWSCAS.2017.8053243","DOI":"10.1109\/MWSCAS.2017.8053243"},{"key":"287_CR13","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1109\/MITS.2022.3204099","volume":"15","author":"W Du","year":"2023","unstructured":"Du W, Li B, Chen J, Lv Y, Li Y (2023) A spatiotemporal hybrid model for airspace complexity prediction. IEEE Intell Transp Syst Mag 15:217\u2013224. https:\/\/doi.org\/10.1109\/MITS.2022.3204099","journal-title":"IEEE Intell Transp Syst Mag"},{"key":"287_CR14","doi-asserted-by":"publisher","first-page":"114618","DOI":"10.1016\/j.jbusres.2024.114618","volume":"177","author":"J Dul","year":"2024","unstructured":"Dul J (2024) A different causal perspective with necessary condition analysis. J Bus Res 177:114618. https:\/\/doi.org\/10.1016\/j.jbusres.2024.114618","journal-title":"J Bus Res"},{"key":"287_CR15","doi-asserted-by":"publisher","first-page":"108327","DOI":"10.1016\/j.buildenv.2021.108327","volume":"206","author":"F Elmaz","year":"2021","unstructured":"Elmaz F, Eyckerman R, Casteels W, Latr\u00e9 S, Hellinckx P (2021) Cnn-lstm architecture for predictive indoor temperature modeling. Build Environ 206:108327. https:\/\/doi.org\/10.1016\/j.buildenv.2021.108327","journal-title":"Build Environ"},{"key":"287_CR16","doi-asserted-by":"publisher","first-page":"e1628","DOI":"10.1002\/ecm.1628","volume":"94","author":"JB Grace","year":"2024","unstructured":"Grace JB (2024) An integrative paradigm for building causal knowledge. Ecol Monogr 94:e1628. https:\/\/doi.org\/10.1002\/ecm.1628","journal-title":"Ecol Monogr"},{"key":"287_CR17","doi-asserted-by":"publisher","unstructured":"Graves A (2012) Long short-term memory. In: Supervised sequence labelling with recurrent neural networks, studies in computational intelligence. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 37\u201345. https:\/\/doi.org\/10.1007\/978-3-642-24797-2_4","DOI":"10.1007\/978-3-642-24797-2_4"},{"key":"287_CR18","doi-asserted-by":"publisher","first-page":"4817","DOI":"10.1109\/TVT.2020.2981959","volume":"69","author":"G Gui","year":"2020","unstructured":"Gui G, Zhou Z, Wang J, Liu F, Sun J (2020) Machine learning aided air traffic flow analysis based on aviation big data. IEEE Trans Veh Technol 69:4817\u20134826. https:\/\/doi.org\/10.1109\/TVT.2020.2981959","journal-title":"IEEE Trans Veh Technol"},{"key":"287_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1021\/ci0342472","volume":"44","author":"DM Hawkins","year":"2004","unstructured":"Hawkins DM (2004) The problem of overfitting. J Chem Inf Comput Sci 44:1\u201312. https:\/\/doi.org\/10.1021\/ci0342472","journal-title":"J Chem Inf Comput Sci"},{"key":"287_CR20","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i16.33908","author":"Y Hu","year":"2025","unstructured":"Hu Y, Liu P, Zhu P, Cheng D, Dai T (2025) Adaptive multi-scale decomposition framework for time series forecasting. Proc AAAI Conf Artif Intell. https:\/\/doi.org\/10.1609\/aaai.v39i16.33908","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"287_CR21","doi-asserted-by":"publisher","unstructured":"Huang S, Zhao Z, Li C, Bai L (2025) TimeKAN: KAN-based frequency decomposition learning architecture for long-term time series forecasting. https:\/\/doi.org\/10.48550\/arXiv.2502.06910","DOI":"10.48550\/arXiv.2502.06910"},{"key":"287_CR22","doi-asserted-by":"publisher","first-page":"9232","DOI":"10.1109\/JIOT.2021.3090265","volume":"9","author":"Y Jiang","year":"2021","unstructured":"Jiang Y, Niu S, Zhang K, Chen B, Xu C, Liu D, Song H (2021) Spatial\u2013temporal graph data mining for iot-enabled air mobility prediction. IEEE Internet Things J 9:9232\u20139240","journal-title":"IEEE Internet Things J"},{"key":"287_CR23","doi-asserted-by":"publisher","unstructured":"Jiang J, Han C, Zhao WX, Wang J (2024) PDFormer: Propagation delay-aware dynamic long-range transformer for traffic flow prediction. https:\/\/doi.org\/10.48550\/arXiv.2301.07945","DOI":"10.48550\/arXiv.2301.07945"},{"key":"287_CR24","doi-asserted-by":"publisher","unstructured":"Lea C, Vidal R, Reiter A, Hager GD (2016) Temporal convolutional networks: a unified approach to action segmentation. https:\/\/doi.org\/10.48550\/arXiv.1608.08242","DOI":"10.48550\/arXiv.1608.08242"},{"key":"287_CR25","doi-asserted-by":"publisher","first-page":"103185","DOI":"10.1016\/j.trc.2021.103185","volume":"128","author":"G Li","year":"2021","unstructured":"Li G, Knoop VL, Van Lint H (2021) Multistep traffic forecasting by dynamic graph convolution: interpretations of real-time spatial correlations. Transp Res C: Emerg Technol 128:103185. https:\/\/doi.org\/10.1016\/j.trc.2021.103185","journal-title":"Transp Res C: Emerg Technol"},{"key":"287_CR26","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.tre.2019.09.012","volume":"131","author":"Y Liu","year":"2019","unstructured":"Liu Y, Liu Yi, Hansen M, Pozdnukhov A, Zhang D (2019) Using machine learning to analyze air traffic management actions: ground delay program case study. Transp Res E: Logist Transp Rev 131:80\u201395. https:\/\/doi.org\/10.1016\/j.tre.2019.09.012","journal-title":"Transp Res E: Logist Transp Rev"},{"key":"287_CR27","doi-asserted-by":"publisher","unstructured":"Liu S, Yu H, Liao C, Li J, Lin W, Liu AX, Dustdar S (2022) Pyraformer: low-complexity pyramidal at- tention for long-range time series modeling and forecasting. Presented at the #PLACEHOLDER_PARENT_METADATA_VALUE#. https:\/\/doi.org\/10.34726\/2945","DOI":"10.34726\/2945"},{"key":"287_CR28","doi-asserted-by":"publisher","unstructured":"Liu Y, Hu T, Zhang H, Wu H, Wang S, Ma L, Long M (2024) iTransformer: inverted transformers are effective for time series forecasting. https:\/\/doi.org\/10.48550\/arXiv.2310.06625","DOI":"10.48550\/arXiv.2310.06625"},{"key":"287_CR29","unstructured":"Luo D, Wang X (2024) Moderntcn: a modern pure convolution structure for general time series analysis. Presented at the The Twelfth International Conference on Learning Representations"},{"key":"287_CR30","doi-asserted-by":"publisher","first-page":"1683","DOI":"10.1109\/TITS.2018.2833452","volume":"20","author":"MCR Mur\u00e7a","year":"2019","unstructured":"Mur\u00e7a MCR, Hansman RJ (2019) Identification, characterization, and prediction of traffic flow patterns in multi-airport systems. IEEE Trans Intell Transp Syst 20:1683\u20131696. https:\/\/doi.org\/10.1109\/TITS.2018.2833452","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"287_CR31","doi-asserted-by":"publisher","first-page":"103067","DOI":"10.1016\/j.aei.2024.103067","volume":"64","author":"CHC Nguyen","year":"2025","unstructured":"Nguyen CHC, Liem RP (2025) Multi-aircraft attention-based model for perceptive arrival transit time prediction. Adv Eng Inform 64:103067. https:\/\/doi.org\/10.1016\/j.aei.2024.103067","journal-title":"Adv Eng Inform"},{"key":"287_CR32","doi-asserted-by":"publisher","unstructured":"Nie Y, Nguyen NH, Sinthong P, Kalagnanam J (2023) A time series is worth 64 words: long-term forecasting with transformers. https:\/\/doi.org\/10.48550\/arXiv.2211.14730","DOI":"10.48550\/arXiv.2211.14730"},{"key":"287_CR33","doi-asserted-by":"publisher","first-page":"122219","DOI":"10.1016\/j.eswa.2023.122219","volume":"238","author":"YA Pan","year":"2024","unstructured":"Pan YA, Guo J, Chen Y, Cheng Q, Li W, Liu Y (2024) A fundamental diagram based hybrid framework for traffic flow estimation and prediction by combining a Markovian model with deep learning. Expert Syst Appl 238:122219. https:\/\/doi.org\/10.1016\/j.eswa.2023.122219","journal-title":"Expert Syst Appl"},{"key":"287_CR34","doi-asserted-by":"publisher","first-page":"119897","DOI":"10.1016\/j.eswa.2023.119897","volume":"223","author":"F P\u00e9rez Moreno","year":"2023","unstructured":"P\u00e9rez Moreno F, G\u00f3mez Comendador VF, Delgado-Aguilera Jurado R, Zamarre\u00f1o Su\u00e1rez M, Janisch D, Arnaldo Vald\u00e9s RM (2023) Methodology of air traffic flow clustering and 3-D prediction of air traffic density in ATC sectors based on machine learning models. Expert Syst Appl 223:119897. https:\/\/doi.org\/10.1016\/j.eswa.2023.119897","journal-title":"Expert Syst Appl"},{"key":"287_CR35","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","volume":"20","author":"F Scarselli","year":"2009","unstructured":"Scarselli F, Gori M, Tsoi AC, Hagenbuchner M, Monfardini G (2009) The graph neural network model. IEEE Trans Neural Netw 20:61\u201380. https:\/\/doi.org\/10.1109\/TNN.2008.2005605","journal-title":"IEEE Trans Neural Netw"},{"key":"287_CR36","doi-asserted-by":"publisher","unstructured":"Shaygan M, Meese C, Li W, Zhao X (George), Nejad M (2022) Traffic prediction using artificial intelligence: review of recent\u00a0advances and emerging opportunities. Transp Res C: Emerg Technol 145:103921. https:\/\/doi.org\/10.1016\/j.trc.2022.103921","DOI":"10.1016\/j.trc.2022.103921"},{"key":"287_CR37","doi-asserted-by":"publisher","unstructured":"Siami-Namini S, Tavakoli N, Namin AS (2019) The performance of LSTM and BiLSTM in forecasting time series. In: 2019 IEEE International Conference on Big Data (Big Data). Presented at the 2019 IEEE International Conference on Big Data (Big Data). IEEE, Los Angeles, CA, USA, pp 3285\u20133292. https:\/\/doi.org\/10.1109\/BigData47090.2019.9005997","DOI":"10.1109\/BigData47090.2019.9005997"},{"key":"287_CR38","doi-asserted-by":"publisher","first-page":"e70035","DOI":"10.1111\/geb.70035","volume":"34","author":"JT Thorson","year":"2025","unstructured":"Thorson JT, Anderson SC, Goddard P, Rooper CN (2025) tinyVAST\u202f: R package with an expressive interface to specify lagged and simultaneous effects in multivariate spatio-temporal models. Glob Ecol Biogeogr 34:e70035. https:\/\/doi.org\/10.1111\/geb.70035","journal-title":"Glob Ecol Biogeogr"},{"key":"287_CR39","doi-asserted-by":"publisher","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2023) Attention is all you need. https:\/\/doi.org\/10.48550\/arXiv.1706.03762","DOI":"10.48550\/arXiv.1706.03762"},{"key":"287_CR40","doi-asserted-by":"publisher","first-page":"152","DOI":"10.4236\/jilsa.2013.53017","volume":"05","author":"Q-G Wang","year":"2013","unstructured":"Wang Q-G, Li X, Qin Q (2013) Feature selection for time series modeling. JILSA 05:152\u2013164. https:\/\/doi.org\/10.4236\/jilsa.2013.53017","journal-title":"JILSA"},{"key":"287_CR41","doi-asserted-by":"publisher","first-page":"103126","DOI":"10.1016\/j.aei.2025.103126","volume":"65","author":"X Wang","year":"2025","unstructured":"Wang X, Dai J, Liu X (2025) A spatial-temporal neural network based on ResNet-Transformer for predicting railroad broken rails. Adv Eng Inform 65:103126. https:\/\/doi.org\/10.1016\/j.aei.2025.103126","journal-title":"Adv Eng Inform"},{"key":"287_CR42","doi-asserted-by":"publisher","unstructured":"Wu H, Xu J, Wang J, Long M (2022) Autoformer: decomposition transformers with auto-correlation for long-term series forecasting. https:\/\/doi.org\/10.48550\/arXiv.2106.13008","DOI":"10.48550\/arXiv.2106.13008"},{"key":"287_CR43","doi-asserted-by":"publisher","first-page":"107603","DOI":"10.1016\/j.atmosres.2024.107603","volume":"310","author":"Z Xing","year":"2024","unstructured":"Xing Z, Wei J, Li Y, Zhang X, Ma M, Yi P, Ju Q, Laux P, Kunstmann H (2024) Disentangling the spatially combined and temporally lagged influences of climate oscillations on seasonal droughts in the East Asian monsoon influenced Poyang Lake Basin. Atmos Res 310:107603. https:\/\/doi.org\/10.1016\/j.atmosres.2024.107603","journal-title":"Atmos Res"},{"key":"287_CR44","doi-asserted-by":"publisher","first-page":"22386","DOI":"10.1109\/TITS.2021.3102983","volume":"23","author":"H Yan","year":"2022","unstructured":"Yan H, Ma X, Pu Z (2022) Learning dynamic and hierarchical traffic spatiotemporal features with transformer. IEEE Trans Intell Transp Syst 23:22386\u201322399. https:\/\/doi.org\/10.1109\/TITS.2021.3102983","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"287_CR45","doi-asserted-by":"publisher","first-page":"101924","DOI":"10.1016\/j.inffus.2023.101924","volume":"100","author":"Z Yan","year":"2023","unstructured":"Yan Z, Yang H, Guo D, Lin Y (2023a) Improving airport arrival flow prediction considering heterogeneous and dynamic network dependencies. Inf Fusion 100:101924. https:\/\/doi.org\/10.1016\/j.inffus.2023.101924","journal-title":"Inf Fusion"},{"key":"287_CR46","doi-asserted-by":"publisher","first-page":"102997","DOI":"10.1016\/j.tre.2022.102997","volume":"170","author":"Z Yan","year":"2023","unstructured":"Yan Z, Yang H, Wu Y, Lin Y (2023b) A multi-view attention-based spatial\u2013temporal network for airport arrival flow prediction. Transp Res E: Logist Transp Rev 170:102997. https:\/\/doi.org\/10.1016\/j.tre.2022.102997","journal-title":"Transp Res E: Logist Transp Rev"},{"key":"287_CR47","doi-asserted-by":"publisher","first-page":"125001","DOI":"10.1016\/j.eswa.2024.125001","volume":"256","author":"J Yan","year":"2024","unstructured":"Yan J, Zhang L, Gao Y, Qu B (2024) GECRAN: graph embedding based convolutional recurrent attention network for traffic flow prediction. Expert Syst Appl 256:125001. https:\/\/doi.org\/10.1016\/j.eswa.2024.125001","journal-title":"Expert Syst Appl"},{"key":"287_CR48","doi-asserted-by":"publisher","unstructured":"Yang H, Du L,\u00a0Zhang G, Ma T\u00a0(2023) A traffic flow dependency and dynamics based deep learning aided approach for network-wide traffic speed propagation prediction.\u00a0Transp Res B Methodol\u00a0167:99\u2013117. https:\/\/doi.org\/10.1016\/j.trb.2022.11.009","DOI":"10.1016\/j.trb.2022.11.009"},{"key":"287_CR49","doi-asserted-by":"publisher","first-page":"102935","DOI":"10.1016\/j.aei.2024.102935","volume":"62","author":"Y Zeng","year":"2024","unstructured":"Zeng Y, Hu M, Chen H, Yuan L, Alam S, Xue D (2024) Improved air traffic flow prediction in terminal areas using a multimodal spatial\u2013temporal network for weather-aware (MST-WA) model. Adv Eng Inform 62:102935. https:\/\/doi.org\/10.1016\/j.aei.2024.102935","journal-title":"Adv Eng Inform"},{"key":"287_CR50","doi-asserted-by":"publisher","first-page":"8996","DOI":"10.1016\/j.jksuci.2022.08.025","volume":"34","author":"S Zhang","year":"2022","unstructured":"Zhang S, Liu Y, Xiao Y, He R (2022) Spatial-temporal upsampling graph convolutional network for daily long-term traffic speed prediction. J King Saud Univ - Comput Inf Sci 34:8996\u20139010. https:\/\/doi.org\/10.1016\/j.jksuci.2022.08.025","journal-title":"J King Saud Univ - Comput Inf Sci"},{"key":"287_CR51","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1007\/s44443-025-00024-3","volume":"37","author":"J Zhang","year":"2025","unstructured":"Zhang J, Wang X, Yang Y, Miao H, Yang S (2025) A novel anomaly detection method for multivariate time series based on spatial-temporal graph learning. J King Saud Univ Comput Inf Sci 37:9. https:\/\/doi.org\/10.1007\/s44443-025-00024-3","journal-title":"J King Saud Univ Comput Inf Sci"},{"key":"287_CR52","doi-asserted-by":"publisher","first-page":"3848","DOI":"10.1109\/TITS.2019.2935152","volume":"21","author":"L Zhao","year":"2020","unstructured":"Zhao L, Song Y, Zhang C, Liu Y, Wang P, Lin T, Deng M, Li H (2020) 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":"287_CR53","doi-asserted-by":"publisher","first-page":"11106","DOI":"10.1609\/aaai.v35i12.17325","volume":"35","author":"H Zhou","year":"2021","unstructured":"Zhou H, Zhang S, Peng J, Zhang S, Li J, Xiong H, Zhang W (2021a) Informer: beyond efficient transformer for long sequence time-series forecasting. AAAI 35:11106\u201311115. https:\/\/doi.org\/10.1609\/aaai.v35i12.17325","journal-title":"AAAI"},{"key":"287_CR54","doi-asserted-by":"publisher","unstructured":"Zhou H, Zhang S, Peng J, Zhang S, Li J, Xiong H, Zhang W (2021b) Informer: beyond efficient transformer for long sequence time-series forecasting. https:\/\/doi.org\/10.48550\/arXiv.2012.07436","DOI":"10.48550\/arXiv.2012.07436"},{"key":"287_CR55","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1007\/s44443-025-00032-3","volume":"37","author":"L Zhu","year":"2025","unstructured":"Zhu L, Gao J, Zhu C, Deng F, He J (2025) Msrnet: multi-level series decomposition and stepwise reconstruction network for load forecasting. J King Saud Univ Comput Inf Sci 37:25. https:\/\/doi.org\/10.1007\/s44443-025-00032-3","journal-title":"J King Saud Univ Comput Inf Sci"},{"key":"287_CR56","doi-asserted-by":"publisher","unstructured":"Zi\u0119ba A, Ramza P (2011) Standard deviation of the mean of autocorrelated observations estimated with the use of the autocorrelation function estimated from the data. Metrol Meas Syst 18. https:\/\/doi.org\/10.2478\/v10178-011-0052-x","DOI":"10.2478\/v10178-011-0052-x"}],"container-title":["Journal of King Saud University Computer and Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-025-00287-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44443-025-00287-w","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-025-00287-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T18:47:24Z","timestamp":1767638844000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44443-025-00287-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,24]]},"references-count":56,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["287"],"URL":"https:\/\/doi.org\/10.1007\/s44443-025-00287-w","relation":{},"ISSN":["1319-1578","2213-1248"],"issn-type":[{"type":"print","value":"1319-1578"},{"type":"electronic","value":"2213-1248"}],"subject":[],"published":{"date-parts":[[2025,11,24]]},"assertion":[{"value":"2 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 November 2025","order":3,"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":"Competing interests"}}],"article-number":"329"}}