{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T23:58:03Z","timestamp":1777507083420,"version":"3.51.4"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2020,9,22]],"date-time":"2020-09-22T00:00:00Z","timestamp":1600732800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,9,22]],"date-time":"2020-09-22T00:00:00Z","timestamp":1600732800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1933123"],"award-info":[{"award-number":["U1933123"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"published-print":{"date-parts":[[2021,5]]},"DOI":"10.1007\/s11227-020-03428-2","type":"journal-article","created":{"date-parts":[[2020,9,22]],"date-time":"2020-09-22T08:02:39Z","timestamp":1600761759000},"page":"4221-4243","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Forecasting air passenger traffic flow based on the two-phase learning model"],"prefix":"10.1007","volume":"77","author":[{"given":"Xinfang","family":"Wu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Xiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gang","family":"Mao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingqian","family":"Du","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiuqing","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2007-2493","authenticated-orcid":false,"given":"Xinzhi","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,9,22]]},"reference":[{"key":"3428_CR1","unstructured":"SITA: Air transport industry insights: the baggage report of 2019. https:\/\/www.sita.aero\/resources\/type\/surveys-reports\/baggage-it-insights-2019 (2019). Accessed 10 Aug 2020"},{"key":"3428_CR2","doi-asserted-by":"publisher","first-page":"902","DOI":"10.1016\/j.ijforecast.2009.06.002","volume":"27","author":"R Fildes","year":"2011","unstructured":"Fildes R, Wei Y, Ismail S (2011) Evaluating the forecasting performance of econometric models of air passenger traffic flows using multiple error measures. Int J Forecast 27:902\u2013922. https:\/\/doi.org\/10.1016\/j.ijforecast.2009.06.002","journal-title":"Int J Forecast"},{"key":"3428_CR3","doi-asserted-by":"publisher","first-page":"42946","DOI":"10.1109\/ACCESS.2019.2907739","volume":"7","author":"J Guo","year":"2019","unstructured":"Guo J, Xie Z, Qin Y, Jia L, Wang Y (2019) Short-term abnormal passenger flow prediction based on the fusion of SVR and LSTM. IEEE Access 7:42946\u201342955. https:\/\/doi.org\/10.1109\/ACCESS.2019.2907739","journal-title":"IEEE Access"},{"key":"3428_CR4","volume-title":"Box and Jenkins: time series analysis, forecasting and control","author":"G Box","year":"2013","unstructured":"Box G (2013) Box and Jenkins: time series analysis, forecasting and control. Palgrave Macmillan, London"},{"issue":"1","key":"3428_CR5","doi-asserted-by":"publisher","first-page":"132","DOI":"10.3141\/1644-14","volume":"1644","author":"BM Williams","year":"1998","unstructured":"Williams BM, Durvasula PK, Brown DE (1998) Urban freeway traffic flow prediction: application of seasonal autoregressive integrated moving average and exponential smoothing models. Transp Res Rec 1644(1):132\u2013141. https:\/\/doi.org\/10.3141\/1644-14","journal-title":"Transp Res Rec"},{"key":"3428_CR6","doi-asserted-by":"publisher","DOI":"10.2307\/2347162","author":"C Chatfield","year":"1978","unstructured":"Chatfield C (1978) The holt-winters forecasting procedure. Appl Stat. https:\/\/doi.org\/10.2307\/2347162","journal-title":"Appl Stat"},{"key":"3428_CR7","unstructured":"Chen Y (2017) The research and applications of time series forecasting methods based on machine learning. Ph.D. thesis, Lanzhou University"},{"key":"3428_CR8","doi-asserted-by":"publisher","first-page":"20050","DOI":"10.1109\/ACCESS.2019.289702810.1109\/ACCESS.2019.2897028","volume":"7","author":"D Qin","year":"2019","unstructured":"Qin D, Yu J, Zou G, Yong R, Zhao Q, Zhang B (2019) A novel combined prediction scheme based on CNN and LSTM for urban pm2.5 concentration. IEEE Access 7:20050\u201320059. https:\/\/doi.org\/10.1109\/ACCESS.2019.289702810.1109\/ACCESS.2019.2897028","journal-title":"IEEE Access"},{"key":"3428_CR9","first-page":"98","volume":"1453","author":"B Smith","year":"1994","unstructured":"Smith B, Demetsky M (1994) Short-term traffic flow prediction: neural network approach. Transp Res Rec 1453:98\u2013104","journal-title":"Transp Res Rec"},{"key":"3428_CR10","doi-asserted-by":"publisher","unstructured":"Ke-wu Y (2009) Study on the forecast of air passenger flow based on SVM regression algorithm. In: 2009 First International Workshop on Database Technology and Applications, pp 325\u2013328. https:\/\/doi.org\/10.1109\/DBTA.2009.33","DOI":"10.1109\/DBTA.2009.33"},{"key":"3428_CR11","doi-asserted-by":"publisher","unstructured":"Laik MN, Choy M, Sen P (2014) Predicting airline passenger load: a case study. In: 2014 IEEE 16th Conference on Business Informatics, vol\u00a01, pp 33\u201338. https:\/\/doi.org\/10.1109\/CBI.2014.39","DOI":"10.1109\/CBI.2014.39"},{"key":"3428_CR12","doi-asserted-by":"publisher","unstructured":"Lu P, Wei F, Yang L (2019) Military escape parachute price prediction for small sample based on xgboost. In: 2019 16th International Conference on Service Systems and Service Management (ICSSSM), pp 1\u20135. https:\/\/doi.org\/10.1109\/ICSSSM.2019.8887752","DOI":"10.1109\/ICSSSM.2019.8887752"},{"key":"3428_CR13","doi-asserted-by":"crossref","unstructured":"Dietterich TG (2000) Ensemble methods in machine learning. In: Dietterich TG (eds) Proceedings of the 1st international workshop on multiple classifier systems (MCS), Cagliari, Italy, pp 1\u201315","DOI":"10.1007\/3-540-45014-9_1"},{"key":"3428_CR14","doi-asserted-by":"publisher","DOI":"10.3390\/a10040139","author":"W Nai","year":"2017","unstructured":"Nai W, Liu L, Wang S, Dong D (2017) An emd-sarima-based modeling approach for air traffic forecasting. Algorithms. https:\/\/doi.org\/10.3390\/a10040139","journal-title":"Algorithms"},{"key":"3428_CR15","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1016\/j.trc.2014.03.016.","volume":"44","author":"X Jiang","year":"2014","unstructured":"Jiang X, Zhang L, Chen XM (2014) Short-term forecasting of high-speed rail demand: a hybrid approach combining ensemble empirical mode decomposition and gray support vector machine with real-world applications in China. Transp Res C Emerg Technol 44:110\u2013127. https:\/\/doi.org\/10.1016\/j.trc.2014.03.016.","journal-title":"Transp Res C Emerg Technol"},{"key":"3428_CR16","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1016\/j.ijforecast.2015.07.002","volume":"32","author":"C Bergmeir","year":"2016","unstructured":"Bergmeir C, Hyndman R, Ben\u00edtez J (2016) Bagging exponential smoothing methods using STL decomposition and Box\u2013Cox transformation. Int J Forecast 32:303\u2013312. https:\/\/doi.org\/10.1016\/j.ijforecast.2015.07.002","journal-title":"Int J Forecast"},{"key":"3428_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jairtraman.2018.05.007.","volume":"71","author":"MR Nieto","year":"2018","unstructured":"Nieto MR, Carmona-Ben\u00edtez RB (2018) Arima + garch + bootstrap forecasting method applied to the airline industry. J Air Transp Manag 71:1\u20138. https:\/\/doi.org\/10.1016\/j.jairtraman.2018.05.007.","journal-title":"J Air Transp Manag"},{"key":"3428_CR18","doi-asserted-by":"publisher","unstructured":"Sulistyowati R, Suhartono, Kuswanto H, Setiawan, Astuti ET (2018) Hybrid forecasting model to predict air passenger and cargo in Indonesia. In: 2018 International Conference on Information and Communications Technology (ICOIACT), pp 442\u2013447. https:\/\/doi.org\/10.1109\/ICOIACT.2018.8350816","DOI":"10.1109\/ICOIACT.2018.8350816"},{"issue":"4","key":"3428_CR19","doi-asserted-by":"publisher","first-page":"900","DOI":"10.2166\/hydro.2020.017.","volume":"22","author":"Xy Bi","year":"2020","unstructured":"Bi Xy, Li B, Lu Wl, Zhou Xz (2020) Daily runoff forecasting based on data-augmented neural network model. J Hydroinform 22(4):900\u2013915. https:\/\/doi.org\/10.2166\/hydro.2020.017.","journal-title":"J Hydroinform"},{"issue":"10","key":"3428_CR20","doi-asserted-by":"publisher","first-page":"5461","DOI":"10.1007\/s11227-018-2452-0.","volume":"74","author":"A Mostafaeipour","year":"2018","unstructured":"Mostafaeipour A, Goli A, Qolipour M (2018) Prediction of air travel demand using a hybrid artificial neural network (ANN) with bat and firefly algorithms: a case study. J Supercomput 74(10):5461\u20135484. https:\/\/doi.org\/10.1007\/s11227-018-2452-0.","journal-title":"J Supercomput"},{"key":"3428_CR21","doi-asserted-by":"publisher","first-page":"3735","DOI":"10.1166\/jctn.2019.8242","volume":"16","author":"S Larabi Marie-Sainte","year":"2019","unstructured":"Larabi Marie-Sainte S, Saba T, Alotaibi S (2019) Air passenger demand forecasting using particle swarm optimization and firefly algorithm. J Comput Theor Nanosci 16:3735\u20133743. https:\/\/doi.org\/10.1166\/jctn.2019.8242","journal-title":"J Comput Theor Nanosci"},{"key":"3428_CR22","doi-asserted-by":"publisher","first-page":"729","DOI":"10.1007\/978-981-10-6385-5_61","volume-title":"Data science","author":"X Liu","year":"2017","unstructured":"Liu X, Huang X, Chen L, Qiu Z, Chen M (2017) Prediction of passenger flow at Sanya airport based on combined methods. In: Zou B, Li M, Wang H, Song X, Xie W, Lu Z (eds) Data science. Springer Singapore, Singapore, pp 729\u2013740"},{"key":"3428_CR23","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.trc.2018.04.025.","volume":"92","author":"F Guo","year":"2018","unstructured":"Guo F, Polak JW, Krishnan R (2018) Predictor fusion for short-term traffic forecasting. Transp Res C Emerg Technol 92:90\u2013100. https:\/\/doi.org\/10.1016\/j.trc.2018.04.025.","journal-title":"Transp Res C Emerg Technol"},{"key":"3428_CR24","doi-asserted-by":"publisher","first-page":"2986","DOI":"10.1007\/s11227-019-03064-5","volume":"76","author":"K Zhao","year":"2020","unstructured":"Zhao K, Li L, Cai et\u00a0al S (2020) An optimized time series combined forecasting method based on neural networks. J Supercomput 7:2986\u20133012. https:\/\/doi.org\/10.1007\/s11227-019-03064-5","journal-title":"J Supercomput"},{"key":"3428_CR25","doi-asserted-by":"crossref","unstructured":"Hierons R (1999) Machine learning. Tom M. Mitchell. published by Mcgrawand#x2010;hill, Maidenhead, U.K., International student edition, 1997. ISBN: 0and#x2010;07and#x2010;115467and#x2010;1, 414 pp. price: U.K. andpound;22.99, soft cover. Software Testing, Verification and Reliability 9(3), 191\u2013193","DOI":"10.1002\/(SICI)1099-1689(199909)9:3<191::AID-STVR184>3.0.CO;2-E"},{"key":"3428_CR26","doi-asserted-by":"publisher","first-page":"8331","DOI":"10.1016\/j.atmosenv.2008.07.020","volume":"42","author":"L Diaz-Robles","year":"2008","unstructured":"Diaz-Robles L, Ortega Bravo JC, Fu J, Reed G, Chow J, Watson J, Moncada J (2008) A hybrid arima and artificial neural networks model to forecast particulate matter in urban areas: the case of Temuco, Chile. Atmos Environ 42:8331\u20138340. https:\/\/doi.org\/10.1016\/j.atmosenv.2008.07.020","journal-title":"Atmos Environ"},{"key":"3428_CR27","unstructured":"Elvira L (2002) Annual electrical peak load forecasting methods with measures of prediction error. Diss Abstr Int 62 Section: B"},{"key":"3428_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.tre.2014.03.009.","volume":"67","author":"J Ruiz-Aguilar","year":"2014","unstructured":"Ruiz-Aguilar J, Turias I, Jim\u00e9nez-Come M (2014) Hybrid approaches based on sarima and artificial neural networks for inspection time series forecasting. Transp Res E Logist Transp Rev 67:1\u201313. https:\/\/doi.org\/10.1016\/j.tre.2014.03.009.","journal-title":"Transp Res E Logist Transp Rev"},{"issue":"99","key":"3428_CR29","first-page":"1","volume":"PP","author":"E Chemali","year":"2017","unstructured":"Chemali E, Kollmeyer P, Preindl M, Ahmed R, Emadi A (2017) Long short-term memory-networks for accurate state of charge estimation of li-ion batteries. IEEE Trans Ind Electron PP(99):1","journal-title":"IEEE Trans Ind Electron"},{"key":"3428_CR30","doi-asserted-by":"publisher","first-page":"75464","DOI":"10.1109\/ACCESS.2019.2919566","volume":"7","author":"J Li","year":"2019","unstructured":"Li J, Li X, He D (2019) A directed acyclic graph network combined with CNN and LSTM for remaining useful life prediction. IEEE Access 7:75464\u201375475. https:\/\/doi.org\/10.1109\/ACCESS.2019.2919566","journal-title":"IEEE Access"},{"key":"3428_CR31","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C (2016) Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","DOI":"10.1145\/2939672.2939785"},{"key":"3428_CR32","doi-asserted-by":"publisher","unstructured":"XingFen W, Xiangbin Y, Yangchun M (2018) Research on user consumption behavior prediction based on improved xgboost algorithm. In: 2018 IEEE International Conference on Big Data (Big Data), pp 4169\u20134175. https:\/\/doi.org\/10.1109\/BigData.2018.8622235","DOI":"10.1109\/BigData.2018.8622235"},{"key":"3428_CR33","unstructured":"BTS: Passengers (all U.S. carriers-all airports). https:\/\/www.bts.gov\/ (2019). Accessed 10 June 2019"},{"issue":"1","key":"3428_CR34","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1008640732416.","volume":"8","author":"PK Chan","year":"1997","unstructured":"Chan PK, Stolfo SJ (1997) On the accuracy of meta-learning for scalable data mining. J Intell Inf Syst 8(1):5\u201328. https:\/\/doi.org\/10.1023\/A:1008640732416.","journal-title":"J Intell Inf Syst"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-020-03428-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-020-03428-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-020-03428-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,21]],"date-time":"2021-09-21T23:50:04Z","timestamp":1632268204000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-020-03428-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,22]]},"references-count":34,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2021,5]]}},"alternative-id":["3428"],"URL":"https:\/\/doi.org\/10.1007\/s11227-020-03428-2","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"value":"0920-8542","type":"print"},{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,22]]},"assertion":[{"value":"2 September 2020","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 September 2020","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"No potential conflict of interest was reported by the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}