{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T23:39:47Z","timestamp":1773704387220,"version":"3.50.1"},"reference-count":100,"publisher":"Springer Science and Business Media LLC","issue":"20","license":[{"start":{"date-parts":[[2024,5,7]],"date-time":"2024-05-07T00:00:00Z","timestamp":1715040000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,5,7]],"date-time":"2024-05-07T00:00:00Z","timestamp":1715040000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2022YFB2602403"],"award-info":[{"award-number":["2022YFB2602403"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Hong Kong Polytechnic University"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2025,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Accurate sector-based air traffic flow predictions are essential for ensuring the safety and efficiency of the air traffic management (ATM) system. However, due to the inherent spatial and temporal dependencies of air traffic flow, it is still a challenging problem. To solve this problem, some methods are proposed considering the relationship between sectors, while the complicated spatiotemporal dynamics and interdependencies between traffic flow of route segments related to the sector are not taken into account. To address this challenge, the attention-enhanced graph convolutional long short-term memory network (AGC-LSTM) model is applied to improve the short-term sector-based traffic flow prediction, in which spatial structures of route segments related to the sector are considered for the first time. Specifically, the graph convolutional networks (GCN)-LSTM network model was employed to capture spatiotemporal dependencies of the flight data, and the attention mechanism is designed to concentrate on the informative features from key nodes at each layer of the AGC-LSTM model. The proposed model is evaluated through a case study of the typical enroute sector in the central\u2013southern region of China. The prediction results show that MAE reduces by 14.4% compared to the best performing GCN-LSTM model among the other five models. Furthermore, the study involves comparative analyses to assess the influence of route segment range, input and output sequence lengths, and time granularities on prediction performance. This study helps air traffic managers predict flight situations more accurately and avoid implementing overly conservative or excessively aggressive flow management measures for the sectors.<\/jats:p>","DOI":"10.1007\/s00521-024-09827-3","type":"journal-article","created":{"date-parts":[[2024,5,7]],"date-time":"2024-05-07T05:02:31Z","timestamp":1715058151000},"page":"14869-14888","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Short-term multi-step-ahead sector-based traffic flow prediction based on the attention-enhanced graph convolutional LSTM network (AGC-LSTM)"],"prefix":"10.1007","volume":"37","author":[{"given":"Ying","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shimin","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linghui","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiwei","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sameer","family":"Alam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4368-9261","authenticated-orcid":false,"given":"Dabin","family":"Xue","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,5,7]]},"reference":[{"key":"9827_CR1","doi-asserted-by":"crossref","first-page":"102010","DOI":"10.1016\/j.jairtraman.2020.102010","volume":"91","author":"A Dixit","year":"2021","unstructured":"Dixit A, Jakhar SK (2021) Airport capacity management: a review and bibliometric analysis. J Air Transp Manag 91:102010","journal-title":"J Air Transp Manag"},{"key":"9827_CR2","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/j.trc.2020.02.007","volume":"114","author":"Y Xu","year":"2020","unstructured":"Xu Y, Prats X, Delahaye D (2020) Synchronised demand-capacity balancing in collaborative air traffic flow management. Transp Res Part C: Emerg Technol 114:359\u2013376","journal-title":"Transp Res Part C: Emerg Technol"},{"issue":"9","key":"9827_CR3","first-page":"633","volume":"19","author":"P Juntama","year":"2022","unstructured":"Juntama P, Delahaye D, Chaimatanan S, Alam S (2022) Hyperheuristic approach based on reinforcement learning for air traffic complexity mitigation. J Aerospace Inf Syst 19(9):633\u2013648","journal-title":"J Aerospace Inf Syst"},{"key":"9827_CR4","unstructured":"ICAO (2016) Doc 4444-Procedures for Air Navigation Services: Air Traffic Management. In: International Aviation Civil Organization Montreal"},{"key":"9827_CR5","doi-asserted-by":"crossref","first-page":"101850","DOI":"10.1016\/j.jairtraman.2020.101850","volume":"87","author":"L Dray","year":"2020","unstructured":"Dray L (2020) An empirical analysis of airport capacity expansion. J Air Transp Manag 87:101850","journal-title":"J Air Transp Manag"},{"issue":"4","key":"9827_CR6","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1016\/j.cja.2022.11.016","volume":"36","author":"Q Cai","year":"2023","unstructured":"Cai Q, Ang HJ, Alam S (2023) Collision risk assessment of reduced aircraft separation minima in procedural airspace using advanced communication and navigation. Chin J Aeronaut 36(4):315\u2013337","journal-title":"Chin J Aeronaut"},{"key":"9827_CR7","doi-asserted-by":"crossref","first-page":"107119","DOI":"10.1016\/j.cie.2021.107119","volume":"154","author":"KK Ng","year":"2021","unstructured":"Ng KK, Chen C-H, Lee CK (2021) Mathematical programming formulations for robust airside terminal traffic flow optimisation problem. Comput Ind Eng 154:107119","journal-title":"Comput Ind Eng"},{"key":"9827_CR8","doi-asserted-by":"crossref","first-page":"101402","DOI":"10.1016\/j.aei.2021.101402","volume":"50","author":"D Xue","year":"2021","unstructured":"Xue D, Hsu L-T, Wu C-L, Lee C-H, Ng KK (2021) Cooperative surveillance systems and digital-technology enabler for a real-time standard terminal arrival schedule displacement. Adv Eng Inform 50:101402","journal-title":"Adv Eng Inform"},{"key":"9827_CR9","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.tre.2019.09.012","volume":"131","author":"Y Liu","year":"2019","unstructured":"Liu Y, Liu Y, Hansen M, Pozdnukhov A, Zhang D (2019) Using machine learning to analyze air traffic management actions: ground delay program case study. Transp Res Part E Logist Transp Rev 131:80\u201395","journal-title":"Transp Res Part E Logist Transp Rev"},{"key":"9827_CR10","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.trc.2011.11.013","volume":"33","author":"CN Glover","year":"2013","unstructured":"Glover CN, Ball MO (2013) Stochastic optimization models for ground delay program planning with equity\u2013efficiency tradeoffs. Transp Res Part C Emerg Technol 33:196\u2013202","journal-title":"Transp Res Part C Emerg Technol"},{"key":"9827_CR11","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.asoc.2018.02.013","volume":"66","author":"K Ng","year":"2018","unstructured":"Ng K, Lee CK, Chan FT, Lv Y (2018) Review on meta-heuristics approaches for airside operation research. Appl Soft Comput 66:104\u2013133","journal-title":"Appl Soft Comput"},{"issue":"5","key":"9827_CR12","doi-asserted-by":"crossref","first-page":"1115","DOI":"10.3390\/en13051115","volume":"13","author":"CG Corlu","year":"2020","unstructured":"Corlu CG, de la Torre R, Serrano-Hernandez A, Juan AA, Faulin J (2020) Optimizing energy consumption in transportation: literature review, insights, and research opportunities. Energies 13(5):1115","journal-title":"Energies"},{"issue":"10","key":"9827_CR13","doi-asserted-by":"crossref","first-page":"1107","DOI":"10.1057\/palgrave.jors.2601190","volume":"52","author":"A Bolat","year":"2001","unstructured":"Bolat A (2001) Models and a genetic algorithm for static aircraft-gate assignment problem. J Oper Res Soc 52(10):1107\u20131120","journal-title":"J Oper Res Soc"},{"key":"9827_CR14","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.trc.2018.01.010","volume":"88","author":"W Ding","year":"2018","unstructured":"Ding W, Zhang Y, Hansen M (2018) Downstream impact of flight rerouting. Transp Res Part C Emer Technol 88:176\u2013186","journal-title":"Transp Res Part C Emer Technol"},{"issue":"4","key":"9827_CR15","doi-asserted-by":"crossref","first-page":"410","DOI":"10.1016\/j.trc.2007.09.001","volume":"16","author":"MV McCrea","year":"2008","unstructured":"McCrea MV, Sherali HD, Trani AA (2008) A probabilistic framework for weather-based rerouting and delay estimations within an airspace planning model. Transp Res Part C Emer Technol 16(4):410\u2013431","journal-title":"Transp Res Part C Emer Technol"},{"key":"9827_CR16","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.trb.2021.05.001","volume":"149","author":"S Birolini","year":"2021","unstructured":"Birolini S, Antunes AP, Cattaneo M, Malighetti P, Paleari S (2021) Integrated flight scheduling and fleet assignment with improved supply-demand interactions. Transp Res Part B Methodol 149:162\u2013180","journal-title":"Transp Res Part B Methodol"},{"key":"9827_CR17","doi-asserted-by":"crossref","first-page":"102186","DOI":"10.1016\/j.tre.2020.102186","volume":"145","author":"ABR Eufr\u00e1sio","year":"2021","unstructured":"Eufr\u00e1sio ABR, Eller RA, Oliveira AV (2021) Are on-time performance statistics worthless? An empirical study of the flight scheduling strategies of Brazilian airlines. Transp Res Part E Logist Transp Rev 145:102186","journal-title":"Transp Res Part E Logist Transp Rev"},{"issue":"2","key":"9827_CR18","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1109\/TITS.2010.2044791","volume":"11","author":"Y Eun","year":"2010","unstructured":"Eun Y, Hwang I, Bang H (2010) Optimal arrival flight sequencing and scheduling using discrete airborne delays. IEEE Trans Intell Transp Syst 11(2):359\u2013373","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"9827_CR19","doi-asserted-by":"crossref","first-page":"108241","DOI":"10.1016\/j.asoc.2021.108241","volume":"115","author":"S Xu","year":"2022","unstructured":"Xu S, Bi W, Zhang A, Mao Z (2022) Optimization of flight test tasks allocation and sequencing using genetic algorithm. Appl Soft Comput 115:108241","journal-title":"Appl Soft Comput"},{"key":"9827_CR20","doi-asserted-by":"crossref","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. Transportation Res Part C Emer Technol 144:103869","journal-title":"Transportation Res Part C Emer Technol"},{"key":"9827_CR21","volume-title":"Introduction to time series analysis and forecasting","author":"DC Montgomery","year":"2015","unstructured":"Montgomery DC, Jennings CL, Kulahci M (2015) Introduction to time series analysis and forecasting. John Wiley & Sons, New Jersey"},{"issue":"12\u201313","key":"9827_CR22","doi-asserted-by":"crossref","first-page":"2096","DOI":"10.1016\/j.neucom.2010.12.032","volume":"74","author":"W-C Hong","year":"2011","unstructured":"Hong W-C (2011) Traffic flow forecasting by seasonal SVR with chaotic simulated annealing algorithm. Neurocomputing 74(12\u201313):2096\u20132107","journal-title":"Neurocomputing"},{"issue":"12","key":"9827_CR23","doi-asserted-by":"crossref","first-page":"4915","DOI":"10.1016\/j.eswa.2013.02.014","volume":"40","author":"RBC Ben\u00edtez","year":"2013","unstructured":"Ben\u00edtez RBC, Paredes RBC, Lodewijks G, Nabais JL (2013) Damp trend Grey Model forecasting method for airline industry. Expert Syst Appl 40(12):4915\u20134921","journal-title":"Expert Syst Appl"},{"key":"9827_CR24","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.trd.2017.03.023","volume":"53","author":"C Huang","year":"2017","unstructured":"Huang C, Xu Y, Johnson ME (2017) Statistical modeling of the fuel flow rate of GA piston engine aircraft using flight operational data. Transp Res Part D: Transp Environ 53:50\u201362","journal-title":"Transp Res Part D: Transp Environ"},{"issue":"2","key":"9827_CR25","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MIS.2009.36","volume":"24","author":"A Halevy","year":"2009","unstructured":"Halevy A, Norvig P, Pereira F (2009) The unreasonable effectiveness of data. IEEE Intell Syst 24(2):8\u201312","journal-title":"IEEE Intell Syst"},{"key":"9827_CR26","doi-asserted-by":"crossref","first-page":"105113","DOI":"10.1016\/j.ast.2019.04.021","volume":"93","author":"Y Lin","year":"2019","unstructured":"Lin Y, Zhang J-w, Liu H (2019) Deep learning based short-term air traffic flow prediction considering temporal\u2013spatial correlation. Aerosp Sci Technol 93:105113","journal-title":"Aerosp Sci Technol"},{"issue":"23","key":"9827_CR27","doi-asserted-by":"crossref","first-page":"4058","DOI":"10.3390\/electronics11234058","volume":"11","author":"H Zang","year":"2022","unstructured":"Zang H, Zhu J, Gao Q (2022) Deep learning architecture for flight flow spatiotemporal prediction in airport network. Electronics 11(23):4058","journal-title":"Electronics"},{"key":"9827_CR28","doi-asserted-by":"crossref","unstructured":"Kim YJ, Choi S, Briceno S, Mavris D (2016) A deep learning approach to flight delay prediction. In: 2016 IEEE\/AIAA 35th Digital Avionics Systems Conference (DASC). IEEE, pp 1\u20136","DOI":"10.1109\/DASC.2016.7778092"},{"key":"9827_CR29","doi-asserted-by":"crossref","first-page":"113246","DOI":"10.1016\/j.dss.2020.113246","volume":"131","author":"X Zhang","year":"2020","unstructured":"Zhang X, Mahadevan S (2020) Bayesian neural networks for flight trajectory prediction and safety assessment. Decis Support Syst 131:113246","journal-title":"Decis Support Syst"},{"issue":"11","key":"9827_CR30","doi-asserted-by":"crossref","first-page":"7242","DOI":"10.1109\/TITS.2020.3004807","volume":"22","author":"Z Shi","year":"2020","unstructured":"Shi Z, Xu M, Pan Q (2020) 4-D flight trajectory prediction with constrained LSTM network. IEEE Trans Intell Transp Syst 22(11):7242\u20137255","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"9827_CR31","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s10479-011-0837-z","volume":"203","author":"HD Sherali","year":"2013","unstructured":"Sherali HD, Hill JM (2013) Configuration of airspace sectors for balancing air traffic controller workload. Ann Oper Res 203:3\u201331","journal-title":"Ann Oper Res"},{"key":"9827_CR32","first-page":"102014","volume":"52","author":"A Di Vaio","year":"2020","unstructured":"Di Vaio A, Varriale L (2020) Blockchain technology in supply chain management for sustainable performance: evidence from the airport industry. Int J Inf Manage 52:102014","journal-title":"Int J Inf Manage"},{"key":"9827_CR33","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.jairtraman.2014.04.001","volume":"39","author":"G Tobaruela","year":"2014","unstructured":"Tobaruela G, Fransen P, Schuster W, Ochieng WY, Majumdar A (2014) Air traffic predictability framework\u2013Development, performance evaluation and application. J Air Transp Manag 39:48\u201358","journal-title":"J Air Transp Manag"},{"issue":"18","key":"9827_CR34","doi-asserted-by":"crossref","first-page":"15429","DOI":"10.1007\/s00521-022-07223-3","volume":"34","author":"Q Sun","year":"2022","unstructured":"Sun Q, Zhang K, Huang K, Li X, Zhang T, Xu T (2022) Enhanced graph convolutional network based on node importance for document-level relation extraction. Neural Comput Appl 34(18):15429\u201315439","journal-title":"Neural Comput Appl"},{"issue":"2","key":"9827_CR35","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1561\/2200000096","volume":"16","author":"L Wu","year":"2023","unstructured":"Wu L, Chen Y, Shen K, Guo X, Gao H, Li S, Pei J, Long B (2023) Graph neural networks for natural language processing: a survey. Found Trends\u00ae Mach Learn 16(2):119\u2013328","journal-title":"Found Trends\u00ae Mach Learn"},{"issue":"16","key":"9827_CR36","doi-asserted-by":"crossref","first-page":"13387","DOI":"10.1007\/s00521-022-07368-1","volume":"34","author":"P Cao","year":"2022","unstructured":"Cao P, Zhu Z, Wang Z, Zhu Y, Niu Q (2022) Applications of graph convolutional networks in computer vision. Neural Comput Appl 34(16):13387\u201313405","journal-title":"Neural Comput Appl"},{"key":"9827_CR37","first-page":"8291","volume":"35","author":"K Han","year":"2022","unstructured":"Han K, Wang Y, Guo J, Tang Y, Wu E (2022) Vision gnn: an image is worth graph of nodes. Adv Neural Inf Process Syst 35:8291\u20138303","journal-title":"Adv Neural Inf Process Syst"},{"key":"9827_CR38","doi-asserted-by":"crossref","unstructured":"Feng C, Liu Z, Lin S, Quek TQ (2019) Attention-based graph convolutional network for recommendation system. In: ICASSP 2019\u20132019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp 7560\u20137564","DOI":"10.1109\/ICASSP.2019.8683050"},{"key":"9827_CR39","doi-asserted-by":"crossref","first-page":"101191","DOI":"10.1016\/j.elerap.2022.101191","volume":"55","author":"S Dhawan","year":"2022","unstructured":"Dhawan S, Singh K, Rabaea A, Batra A (2022) ImprovedGCN: an efficient and accurate recommendation system employing lightweight graph convolutional networks in social media. Electron Commer Res Appl 55:101191","journal-title":"Electron Commer Res Appl"},{"issue":"33","key":"9827_CR40","doi-asserted-by":"crossref","first-page":"24015","DOI":"10.1007\/s00521-023-08974-3","volume":"35","author":"T Vo","year":"2023","unstructured":"Vo T (2023) An integrated fuzzy neural supervision and attention-based graph neural network for improving network clustering. Neural Comput Appl 35(33):24015\u201324035","journal-title":"Neural Comput Appl"},{"issue":"127","key":"9827_CR41","first-page":"1","volume":"24","author":"A Tsitsulin","year":"2023","unstructured":"Tsitsulin A, Palowitch J, Perozzi B, M\u00fcller E (2023) Graph clustering with graph neural networks. J Mach Learn Res 24(127):1\u201321","journal-title":"J Mach Learn Res"},{"key":"9827_CR42","doi-asserted-by":"crossref","first-page":"117921","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","journal-title":"Expert Syst Appl"},{"issue":"3","key":"9827_CR43","doi-asserted-by":"crossref","first-page":"100","DOI":"10.3390\/ijgi12030100","volume":"12","author":"W Jiang","year":"2023","unstructured":"Jiang W, Luo J, He M, Gu W (2023) Graph neural network for traffic forecasting: the research progress. ISPRS Int J Geo Inf 12(3):100","journal-title":"ISPRS Int J Geo Inf"},{"key":"9827_CR44","doi-asserted-by":"crossref","unstructured":"Li Z, Xiong G, Chen Y, Lv Y, Hu B, Zhu F, Wang F-Y (2019) A hybrid deep learning approach with GCN and LSTM for traffic flow prediction. In: 2019 IEEE intelligent transportation systems conference (ITSC). IEEE, pp 1929\u20131933","DOI":"10.1109\/ITSC.2019.8916778"},{"key":"9827_CR45","doi-asserted-by":"crossref","unstructured":"He Y, Zhao Y, Wang H, Tsui KL (2020) GC-LSTM: a deep spatiotemporal model for passenger flow forecasting of high-speed rail network. In: 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC). IEEE, pp 1\u20136","DOI":"10.1109\/ITSC45102.2020.9294700"},{"key":"9827_CR46","doi-asserted-by":"crossref","unstructured":"Guo J, Song C, Wang H (2019) A multi-step traffic speed forecasting model based on graph convolutional LSTM. In: 2019 Chinese Automation Congress (CAC). IEEE, pp 2466\u20132471","DOI":"10.1109\/CAC48633.2019.8997248"},{"key":"9827_CR47","doi-asserted-by":"crossref","unstructured":"Du W, Chen S, Li Z, Cao X, Lv Y (2023) A spatial-temporal approach for multi-airport traffic flow prediction through causality graphs. In: IEEE Transactions on Intelligent Transportation Systems","DOI":"10.1109\/TITS.2023.3308903"},{"key":"9827_CR48","doi-asserted-by":"crossref","first-page":"104521","DOI":"10.1016\/j.trc.2024.104521","volume":"160","author":"B Li","year":"2024","unstructured":"Li B, Li Z, Chen J, Yan Y, Lv Y, Du W (2024) MAST-GNN: A multimodal adaptive spatio-temporal graph neural network for airspace complexity prediction. Transp Res Part C Emerg Technol 160:104521","journal-title":"Transp Res Part C Emerg Technol"},{"key":"9827_CR49","first-page":"100053","volume":"5","author":"A Jardines","year":"2021","unstructured":"Jardines A, Soler M, Cervantes A, Garc\u00eda-Heras J, Simarro J (2021) Convection indicator for pre-tactical air traffic flow management using neural networks. Mach Learn Appl 5:100053","journal-title":"Mach Learn Appl"},{"key":"9827_CR50","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.trc.2016.05.014","volume":"69","author":"KD Kuhn","year":"2016","unstructured":"Kuhn KD (2016) A methodology for identifying similar days in air traffic flow management initiative planning. Transp Res Part C Emer Technol 69:1\u201315","journal-title":"Transp Res Part C Emer Technol"},{"key":"9827_CR51","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.tra.2017.09.027","volume":"114","author":"A Jacquillat","year":"2018","unstructured":"Jacquillat A, Odoni AR (2018) A roadmap toward airport demand and capacity management. Transp Res Part A Policy Pract 114:168\u2013185","journal-title":"Transp Res Part A Policy Pract"},{"key":"9827_CR52","doi-asserted-by":"crossref","first-page":"102872","DOI":"10.1016\/j.trc.2020.102872","volume":"121","author":"M Soltani","year":"2020","unstructured":"Soltani M, Ahmadi S, Akgunduz A, Bhuiyan N (2020) An eco-friendly aircraft taxiing approach with collision and conflict avoidance. Transp Res Part C Emerg Technol 121:102872","journal-title":"Transp Res Part C Emerg Technol"},{"issue":"3","key":"9827_CR53","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1080\/01441647.2015.1077287","volume":"36","author":"W Wattanacharoensil","year":"2016","unstructured":"Wattanacharoensil W, Schuckert M, Graham A (2016) An airport experience framework from a tourism perspective. Transp Rev 36(3):318\u2013340","journal-title":"Transp Rev"},{"issue":"4","key":"9827_CR54","doi-asserted-by":"crossref","first-page":"317","DOI":"10.69554\/OSRQ1306","volume":"9","author":"V Harrison","year":"2015","unstructured":"Harrison V (2015) Delivering a first class travel experience for passengers. J Airport Manage 9(4):317\u2013326","journal-title":"J Airport Manage"},{"key":"9827_CR55","doi-asserted-by":"crossref","first-page":"44059","DOI":"10.1109\/ACCESS.2018.2864157","volume":"6","author":"C Badii","year":"2018","unstructured":"Badii C, Nesi P, Paoli I (2018) Predicting available parking slots on critical and regular services by exploiting a range of open data. IEEE Access 6:44059\u201344071","journal-title":"IEEE Access"},{"key":"9827_CR56","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.tra.2016.11.010","volume":"95","author":"N Ivanov","year":"2017","unstructured":"Ivanov N, Netjasov F, Jovanovi\u0107 R, Starita S, Strauss A (2017) Air traffic flow management slot allocation to minimize propagated delay and improve airport slot adherence. Transp Res Part A Policy Pract 95:183\u2013197","journal-title":"Transp Res Part A Policy Pract"},{"issue":"7","key":"9827_CR57","doi-asserted-by":"crossref","first-page":"e2022SW003144","DOI":"10.1029\/2022SW003144","volume":"20","author":"D Xue","year":"2022","unstructured":"Xue D, Yang J, Liu Z (2022) Potential impact of GNSS positioning errors on the satellite-navigation-based air traffic management. Space Weather 20(7):e2022SW003144","journal-title":"Space Weather"},{"issue":"3","key":"9827_CR58","doi-asserted-by":"crossref","first-page":"1506","DOI":"10.3390\/app12031506","volume":"12","author":"EA Alharbi","year":"2022","unstructured":"Alharbi EA, Abdel-Malek LL, Milne RJ, Wali AM (2022) Analytical model for enhancing the adoptability of continuous descent approach at airports. Appl Sci 12(3):1506","journal-title":"Appl Sci"},{"key":"9827_CR59","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.trc.2017.02.024","volume":"79","author":"NG Polson","year":"2017","unstructured":"Polson NG, Sokolov VO (2017) Deep learning for short-term traffic flow prediction. Transp Res Part C Emer Technol 79:1\u201317","journal-title":"Transp Res Part C Emer Technol"},{"key":"9827_CR60","first-page":"2457","volume":"2019","author":"J Li","year":"2017","unstructured":"Li J, Wang J (2017) Short term traffic flow prediction based on deep learning. CICTP 2019:2457\u20132469","journal-title":"CICTP"},{"issue":"2","key":"9827_CR61","doi-asserted-by":"crossref","first-page":"196","DOI":"10.30518\/jav.1307741","volume":"7","author":"\u00d6O Dursun","year":"2023","unstructured":"Dursun \u00d6O (2023) Air-traffic flow prediction with deep learning: a case study for Diyarbak\u0131r airport. J Aviat 7(2):196\u2013203","journal-title":"J Aviat"},{"key":"9827_CR62","first-page":"1","volume":"2020","author":"Z Yang","year":"2020","unstructured":"Yang Z, Wang Y, Li J, Liu L, Ma J, Zhong Y (2020) Airport arrival flow prediction considering meteorological factors based on deep-learning methods. Complexity 2020:1\u201311","journal-title":"Complexity"},{"key":"9827_CR63","doi-asserted-by":"crossref","first-page":"884485","DOI":"10.3389\/frai.2022.884485","volume":"5","author":"X Zhu","year":"2022","unstructured":"Zhu X, Lin Y, He Y, Tsui K-L, Chan PW, Li L (2022) Short-term nationwide airport throughput prediction with graph attention recurrent neural network. Front Artif Intell 5:884485","journal-title":"Front Artif Intell"},{"issue":"1","key":"9827_CR64","doi-asserted-by":"crossref","first-page":"11","DOI":"10.3390\/aerospace9010011","volume":"9","author":"Z Yan","year":"2021","unstructured":"Yan Z, Yang H, Li F, Lin Y (2021) A deep learning approach for short-term airport traffic flow prediction. Aerospace 9(1):11","journal-title":"Aerospace"},{"key":"9827_CR65","doi-asserted-by":"crossref","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 (2023) A multi-view attention-based spatial-temporal network for airport arrival flow prediction. Transp Res Part E Logist Transp Rev 170:102997","journal-title":"Transp Res Part E Logist Transp Rev"},{"key":"9827_CR66","doi-asserted-by":"crossref","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 (2023) Improving airport arrival flow prediction considering heterogeneous and dynamic network dependencies. Inf Fus 100:101924","journal-title":"Inf Fus"},{"issue":"4","key":"9827_CR67","doi-asserted-by":"crossref","first-page":"882","DOI":"10.1287\/trsc.2019.0962","volume":"54","author":"S Starita","year":"2020","unstructured":"Starita S, Strauss AK, Fei X, Jovanovi\u0107 R, Ivanov N, Pavlovi\u0107 G, Fichert F (2020) Air traffic control capacity planning under demand and capacity provision uncertainty. Transp Sci 54(4):882\u2013896","journal-title":"Transp Sci"},{"key":"9827_CR68","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.cie.2014.11.026","volume":"80","author":"Y Hu","year":"2015","unstructured":"Hu Y, Xu B, Bard JF, Chi H (2015) Optimization of multi-fleet aircraft routing considering passenger transiting under airline disruption. Comput Ind Eng 80:132\u2013144","journal-title":"Comput Ind Eng"},{"key":"9827_CR69","doi-asserted-by":"crossref","first-page":"123","DOI":"10.4324\/9781315092898-9","volume-title":"Human workload in aviation. Human error in aviation","author":"BH Kantowitz","year":"2017","unstructured":"Kantowitz BH, Casper PA (2017) Human workload in aviation. Human error in aviation. Routledge, London, pp 123\u2013153"},{"issue":"2","key":"9827_CR70","doi-asserted-by":"crossref","first-page":"1004","DOI":"10.1109\/TAES.2018.2867698","volume":"55","author":"VP Jilkov","year":"2018","unstructured":"Jilkov VP, Ledet JH, Li XR (2018) Multiple model method for aircraft conflict detection and resolution in intent and weather uncertainty. IEEE Trans Aerosp Electron Syst 55(2):1004\u20131020","journal-title":"IEEE Trans Aerosp Electron Syst"},{"key":"9827_CR71","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.ast.2015.02.018","volume":"43","author":"Y Matsuno","year":"2015","unstructured":"Matsuno Y, Tsuchiya T, Wei J, Hwang I, Matayoshi N (2015) Stochastic optimal control for aircraft conflict resolution under wind uncertainty. Aerosp Sci Technol 43:77\u201388","journal-title":"Aerosp Sci Technol"},{"key":"9827_CR72","doi-asserted-by":"crossref","unstructured":"Metzger U, Parasuraman R (2017) Automation in future air traffic management: Effects of decision aid reliability on controller performance and mental workload. Decision Making in Aviation, Routledge, pp 345\u2013360","DOI":"10.4324\/9781315095080-22"},{"key":"9827_CR73","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.trc.2018.07.008","volume":"95","author":"X Cao","year":"2018","unstructured":"Cao X, Zhu X, Tian Z, Chen J, Wu D, Du W (2018) A knowledge-transfer-based learning framework for airspace operation complexity evaluation. Transp Res Part C Emer Technol 95:61\u201381","journal-title":"Transp Res Part C Emer Technol"},{"issue":"8","key":"9827_CR74","doi-asserted-by":"crossref","first-page":"11739","DOI":"10.1109\/TITS.2021.3106779","volume":"23","author":"B Li","year":"2021","unstructured":"Li B, Du W, Zhang Y, Chen J, Tang K, Cao X (2021) A deep unsupervised learning approach for airspace complexity evaluation. IEEE Trans Intell Transp Syst 23(8):11739\u201311751","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"5","key":"9827_CR75","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.23919\/JSEE.2022.000109","volume":"33","author":"J Tang","year":"2022","unstructured":"Tang J, Liu G, Pan Q (2022) Review on artificial intelligence techniques for improving representative air traffic management capability. J Syst Eng Electron 33(5):1123\u20131134","journal-title":"J Syst Eng Electron"},{"key":"9827_CR76","doi-asserted-by":"crossref","first-page":"105274","DOI":"10.1016\/j.knosys.2019.105274","volume":"199","author":"X Du","year":"2020","unstructured":"Du X, Lu Z, Wu D (2020) An intelligent recognition model for dynamic air traffic decision-making. Knowl-Based Syst 199:105274","journal-title":"Knowl-Based Syst"},{"key":"9827_CR77","unstructured":"Shi-Garrier L, Delahaye D, Bouaynaya NC (2021) Predicting air traffic congested areas with long short-term memory networks. In: Fourteenth USA\/Europe Air Traffic Management Research and Development Seminar (ATM2021)."},{"key":"9827_CR78","first-page":"1","volume":"2021","author":"H Xie","year":"2021","unstructured":"Xie H, Zhang M, Ge J, Dong X, Chen H (2021) Learning air traffic as images: a deep convolutional neural network for airspace operation complexity evaluation. Complexity 2021:1\u201316","journal-title":"Complexity"},{"issue":"10","key":"9827_CR79","doi-asserted-by":"crossref","first-page":"568","DOI":"10.3390\/aerospace9100568","volume":"9","author":"D Sui","year":"2022","unstructured":"Sui D, Liu K, Li Q (2022) Dynamic prediction of air traffic situation in large-scale airspace. Aerospace 9(10):568","journal-title":"Aerospace"},{"key":"9827_CR80","doi-asserted-by":"crossref","first-page":"104225","DOI":"10.1016\/j.trc.2023.104225","volume":"153","author":"Q Xu","year":"2023","unstructured":"Xu Q, Pang Y, Liu Y (2023) Air traffic density prediction using Bayesian ensemble graph attention network (BEGAN). Transp Res Part C Emer Technol 153:104225","journal-title":"Transp Res Part C Emer Technol"},{"key":"9827_CR81","unstructured":"Ma C, Alam S, Cai Q, Delahaye D (2022) Sector entry flow prediction based on graph convolutional networks. In: International Conference on Research in Air Transportation."},{"key":"9827_CR82","doi-asserted-by":"crossref","unstructured":"Brito IR, Rocha Murca MC, Oliveira Md, Oliveira AV (2021) A Machine Learning-based Predictive Model of Airspace Sector Occupancy. In: AIAA AVIATION 2021 FORUM. p 2324","DOI":"10.2514\/6.2021-2324"},{"issue":"1","key":"9827_CR83","doi-asserted-by":"crossref","first-page":"5037","DOI":"10.1149\/10701.5037ecst","volume":"107","author":"TV Asirvadam","year":"2022","unstructured":"Asirvadam TV, Rao S, Balachander T (2022) Predicting air traffic density in an air traffic control sector. ECS Trans 107(1):5037","journal-title":"ECS Trans"},{"key":"9827_CR84","doi-asserted-by":"crossref","first-page":"148019","DOI":"10.1109\/ACCESS.2019.2945821","volume":"7","author":"H Liu","year":"2019","unstructured":"Liu H, Lin Y, Chen Z, Guo D, Zhang J, Jing H (2019) Research on the air traffic flow prediction using a deep learning approach. IEEE Access 7:148019\u2013148030","journal-title":"IEEE Access"},{"key":"9827_CR85","doi-asserted-by":"crossref","first-page":"119897","DOI":"10.1016\/j.eswa.2023.119897","volume":"223","author":"FP Moreno","year":"2023","unstructured":"Moreno FP, Comendador VFG, Jurado RD-A, Su\u00e1rez MZ, Janisch D, Vald\u00e9s RMA (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","journal-title":"Expert Syst Appl"},{"issue":"9","key":"9827_CR86","doi-asserted-by":"crossref","first-page":"2933","DOI":"10.1109\/TITS.2017.2766683","volume":"19","author":"Y Hong","year":"2017","unstructured":"Hong Y, Choi B, Lee K, Kim Y (2017) Dynamic robust sequencing and scheduling under uncertainty for the point merge system in terminal airspace. IEEE Trans Intell Transp Syst 19(9):2933\u20132943","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"9827_CR87","unstructured":"Delahaye D, Ma C, Alam S, Cai Q (2022) Air traffic flow representation and prediction using transformer in flow-centric airspace. In: SESAR Innovation Days."},{"issue":"8","key":"9827_CR88","doi-asserted-by":"crossref","first-page":"2174","DOI":"10.1002\/atr.1453","volume":"50","author":"D Chen","year":"2016","unstructured":"Chen D, Hu M, Ma Y, Yin J (2016) A network-based dynamic air traffic flow model for short-term en route traffic prediction. J Adv Transp 50(8):2174\u20132192","journal-title":"J Adv Transp"},{"key":"9827_CR89","doi-asserted-by":"crossref","first-page":"110135","DOI":"10.1016\/j.knosys.2022.110135","volume":"260","author":"Y Zhang","year":"2023","unstructured":"Zhang Y, Lu Z, Wang J, Chen L (2023) FCM-GCN-based upstream and downstream dependence model for air traffic flow networks. Knowl-Based Syst 260:110135","journal-title":"Knowl-Based Syst"},{"key":"9827_CR90","doi-asserted-by":"crossref","first-page":"102301","DOI":"10.1016\/j.jairtraman.2022.102301","volume":"106","author":"K Cai","year":"2023","unstructured":"Cai K, Shen Z, Luo X, Li Y (2023) Temporal attention aware dual-graph convolution network for air traffic flow prediction. J Air Transp Manag 106:102301","journal-title":"J Air Transp Manag"},{"key":"9827_CR91","doi-asserted-by":"crossref","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","journal-title":"Neurocomputing"},{"key":"9827_CR92","doi-asserted-by":"crossref","unstructured":"Pham V, Bluche T, Kermorvant C, Louradour J (2014) Dropout improves recurrent neural networks for handwriting recognition. In: 2014 14th international conference on frontiers in handwriting recognition. IEEE, pp 285\u2013290","DOI":"10.1109\/ICFHR.2014.55"},{"key":"9827_CR93","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1162\/tacl_a_00101","volume":"4","author":"E Kiperwasser","year":"2016","unstructured":"Kiperwasser E, Goldberg Y (2016) Simple and accurate dependency parsing using bidirectional LSTM feature representations. Trans Assoc Comput Linguist 4:313\u2013327","journal-title":"Trans Assoc Comput Linguist"},{"issue":"3","key":"9827_CR94","doi-asserted-by":"crossref","first-page":"e2022SW003381","DOI":"10.1029\/2022SW003381","volume":"21","author":"D Xue","year":"2023","unstructured":"Xue D, Yang J, Liu Z, Yu S (2023) Examining the economic costs of the 2003 Halloween storm effects on the North Hemisphere aviation using flight data in 2019. Space Weather 21(3):e2022SW003381","journal-title":"Space Weather"},{"issue":"4","key":"9827_CR95","doi-asserted-by":"publisher","first-page":"1841","DOI":"10.1007\/s10291-017-0657-y","volume":"21","author":"BS Ali","year":"2017","unstructured":"Ali BS, Ochieng WY, Zainudin R (2017) An analysis and model for Automatic Dependent Surveillance Broadcast (ADS-B) continuity. GPS Solut 21(4):1841\u20131854\u00a0","journal-title":"GPS Solut"},{"key":"9827_CR96","unstructured":"Jovanovic R, Babic O, Toic V, To\u0161ic V (2015) Pricing to reconcile predictability, efficiency and equity in ATM. In: Proceedings of the 11th USA\/Europe ATM R&D Seminar."},{"key":"9827_CR97","doi-asserted-by":"crossref","unstructured":"Koelman H, Koelle R, Shetty K, Gulding J (2019) Comparison of ATFM Practices and Performance in The US and Europe (2015\u20132018). In: 2019 Integrated Communications, Navigation and Surveillance Conference (ICNS). IEEE, pp 1\u201316","DOI":"10.1109\/ICNSURV.2019.8735162"},{"key":"9827_CR98","doi-asserted-by":"crossref","unstructured":"Masalonis A, Mulgund S, Song L, Wanke C, Zobell S (2004) Using probabilistic demand predictions for traffic flow management decision support. In: AIAA guidance, navigation, and control conference and exhibit. p 5231","DOI":"10.2514\/6.2004-5231"},{"key":"9827_CR99","doi-asserted-by":"crossref","unstructured":"Gilbo E, Smith S (2007) A new model to improve aggregate air traffic demand predictions. In: AIAA Guidance, Navigation and Control Conference and Exhibit. p 6450","DOI":"10.2514\/6.2007-6450"},{"key":"9827_CR100","doi-asserted-by":"crossref","unstructured":"Lee H, Jung YC, Zelinski SJ, Zhu Z, Hosagrahara V (2019) Fast-Time Simulation for Evaluating the Impact of Estimated Flight Ready Time Uncertainty on Surface Metering. In: 2019 IEEE\/AIAA 38th Digital Avionics Systems Conference (DASC). IEEE, pp 1\u201310","DOI":"10.1109\/DASC43569.2019.9081711"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09827-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-09827-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09827-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T11:05:55Z","timestamp":1751886355000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-09827-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,7]]},"references-count":100,"journal-issue":{"issue":"20","published-print":{"date-parts":[[2025,7]]}},"alternative-id":["9827"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-09827-3","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,7]]},"assertion":[{"value":"16 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 April 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 May 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 May 2024","order":4,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Update","order":5,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The original online version of this article was revised to correct the subheadings of section 3.2.","order":6,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}