{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T01:59:02Z","timestamp":1772675942293,"version":"3.50.1"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2019,3,28]],"date-time":"2019-03-28T00:00:00Z","timestamp":1553731200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/100010661","name":"Horizon 2020 Framework Programme","doi-asserted-by":"publisher","award":["No:687591 (datAcron)"],"award-info":[{"award-number":["No:687591 (datAcron)"]}],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010661","name":"Horizon 2020 Framework Programme","doi-asserted-by":"publisher","award":["No:699299 (DART)"],"award-info":[{"award-number":["No:699299 (DART)"]}],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Data Sci Anal"],"published-print":{"date-parts":[[2020,3]]},"DOI":"10.1007\/s41060-019-00182-4","type":"journal-article","created":{"date-parts":[[2019,3,28]],"date-time":"2019-03-28T13:02:40Z","timestamp":1553778160000},"page":"215-228","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Semantic-aware aircraft trajectory prediction using flight plans"],"prefix":"10.1007","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3462-0745","authenticated-orcid":false,"given":"Harris","family":"Georgiou","sequence":"first","affiliation":[]},{"given":"Nikos","family":"Pelekis","sequence":"additional","affiliation":[]},{"given":"Stylianos","family":"Sideridis","sequence":"additional","affiliation":[]},{"given":"David","family":"Scarlatti","sequence":"additional","affiliation":[]},{"given":"Yannis","family":"Theodoridis","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,3,28]]},"reference":[{"key":"182_CR1","doi-asserted-by":"crossref","unstructured":"Ankerst, M., Breunig, M., et al.: Optics: ordering points to identify the clustering structure. In: Proceedings of SIGMOD 1999 (1999)","DOI":"10.1145\/304182.304187"},{"key":"182_CR2","doi-asserted-by":"crossref","unstructured":"Ayhan, S., Samet, H.: Aircraft trajectory prediction made easy with predictive analytics. In: Proceedings of ACM SIGKDD 2016 (2016)","DOI":"10.1145\/2939672.2939694"},{"key":"182_CR3","doi-asserted-by":"crossref","unstructured":"Ayhan, S., Samet, H.: Time series clustering of weather observations in predicting climb phase of aircraft trajectories. In: Proceedings of the IWCTS 2016 (2016)","DOI":"10.1145\/3003965.3003968"},{"key":"182_CR4","volume-title":"Classification and Regression Trees","author":"L Breiman","year":"1984","unstructured":"Breiman, L., Friedman, J., Olsen, R., Stone, C.: Classification and Regression Trees. CRC Press, Boca Raton (1984)"},{"key":"182_CR5","doi-asserted-by":"crossref","unstructured":"Chen, L., Ng, R.: On the marriage of edit distance and lp norms. In: Proceedings of VLDB 2004 (2004)","DOI":"10.1016\/B978-012088469-8.50070-X"},{"issue":"8","key":"182_CR6","doi-asserted-by":"publisher","first-page":"787","DOI":"10.1016\/j.compind.2011.05.006","volume":"62","author":"X Chen","year":"2011","unstructured":"Chen, X., Landry, S., Nof, S.: A framework of enroute air traffic conflict detection and resolution through complex network analysis. Comput. Ind. 62(8), 787\u2013794 (2011)","journal-title":"Comput. Ind."},{"key":"182_CR7","unstructured":"Cheng, T., Cui, D., Cheng, P.: Data mining for air traffic flow forecasting: a hybrid model of neural network and statistical analysis. In: Proceedings of the ITSC 2003 (2003)"},{"key":"182_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.dss.2016.05.004","volume":"88","author":"CD Ciccio","year":"2016","unstructured":"Ciccio, C.D., var\u00a0der Aa, H., Cabanillas, C., et al.: Detecting flight trajectory anomalies and predicting diversions in freight transportation. Decis. Support Syst. 88, 1\u201317 (2016)","journal-title":"Decis. Support Syst."},{"key":"182_CR9","doi-asserted-by":"crossref","unstructured":"Coppenbarger, R.: En route climb trajectory prediction enhancement using airplane flight-planning information. In: American Institute of Aeronautics and Astronautics (AIAA-99-4147) (1999)","DOI":"10.2514\/6.1999-4147"},{"key":"182_CR10","doi-asserted-by":"crossref","unstructured":"de Leege, A., Paassen, M.V., Mulder, M.: A machine learning approach to trajectory prediction. In: Proceedings of AIAA GNC 2013 (2013)","DOI":"10.2514\/6.2013-4782"},{"key":"182_CR11","unstructured":"Enea, G., Poretta, M.: A comparison of 4d-trajectory operations envisioned for nextgen and sesar. In: Proceedings of the ICAS 2012 (2012)"},{"key":"182_CR12","unstructured":"Fablec, Y.L., Alliot, J.: Using neural networks to predict aircraft trajectories. In: Proceedings of the ICIS 1999 (1999)"},{"key":"182_CR13","unstructured":"Georgiou, H., Karagiorgou, S., Kontoulis, Y., Pelekis, N., Petrou, P., Scarlatti, D., Theodoridis, Y.: Moving objects analytics\u2014survey on future location and trajectory prediction methods. Tech. rep., Data Science Lab, University of Piraeus, Greece (2018). \narXiv:1807.04639"},{"key":"182_CR14","doi-asserted-by":"crossref","unstructured":"Gong, C., McNally, D.: A methodology for automated trajectory prediction analysis. In: AIAA Guidance, Navigation, and Control Conference and Exhibit (2004)","DOI":"10.2514\/6.2004-4788"},{"key":"182_CR15","unstructured":"Hadjaz, A., Marceau, G., Saveant, P., etal.: Online learning for ground trajectory prediction (2012). CoRR \narXiv:1212.3998"},{"key":"182_CR16","unstructured":"Hamed, M., Gianazza, D., Serrurier, M., Durand, N.: Statistical prediction of aircraft trajectory: regression methods vs point-mass model. In: Proceedings of the ATM 2013 (2013)"},{"key":"182_CR17","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4614-7138-7","volume-title":"An Introduction to Statistical Learning with Applications in R","author":"G James","year":"2013","unstructured":"James, G., Witten, D., Hastie, T., Tibshirani, R.: An Introduction to Statistical Learning with Applications in R. Springer, Berlin (2013)"},{"key":"182_CR18","unstructured":"Krumm, J., Horvitz, E.: Predestination: inferring destinations from partial trajectories. In: Proceedings of the UbiComp 2003 (2003)"},{"key":"182_CR19","doi-asserted-by":"publisher","DOI":"10.1002\/0471660264","volume-title":"Combing Pattern Classifiers\u2014Methods and Algorithms","author":"L Kuncheva","year":"2004","unstructured":"Kuncheva, L.: Combing Pattern Classifiers\u2014Methods and Algorithms. Wiley, Hoboken (2004)"},{"key":"182_CR20","unstructured":"Liu, Y., Hansen, M.: Predicting aircraft trajectories: a deep generative convolutional recurrent neural networks approach. Tech. rep., Institute of Transportation Studies, University of California (2018). \narXiv:1812.11670"},{"key":"182_CR21","first-page":"361","volume":"12","author":"W Loh","year":"2002","unstructured":"Loh, W.: Regression trees with unbiased variable selection and interaction detection. Stat. Sin. 12, 361\u2013386 (2002)","journal-title":"Stat. Sin."},{"key":"182_CR22","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.ast.2015.02.018","volume":"43","author":"Y Matsuno","year":"2015","unstructured":"Matsuno, Y., Tachiya, T., Wei, J., et al.: Stochastic optimal control for aircraft conflict resolution under wind uncertainty. Aerosp. Sci. Technol. 43, 77\u201388 (2015)","journal-title":"Aerosp. Sci. Technol."},{"issue":"2","key":"182_CR23","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1007\/s10707-016-0266-x","volume":"21","author":"K Patroumpas","year":"2017","unstructured":"Patroumpas, K., Alevizos, E., Artikis, A., Vodas, M., Pelekis, N., Theodoridis, Y.: Online event recognition from moving vessel trajectories. Geoinformatica 21(2), 389\u2013427 (2017)","journal-title":"Geoinformatica"},{"key":"182_CR24","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4939-0392-4","volume-title":"Mobility Data Management and Exploration","author":"N Pelekis","year":"2014","unstructured":"Pelekis, N., Theodoridis, Y.: Mobility Data Management and Exploration. Springer, Berlin (2014)"},{"key":"182_CR25","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1186\/s13634-016-0355-x","volume":"2016","author":"J Qiu","year":"2016","unstructured":"Qiu, J., Wu, Q., Ding, G., et al.: A survey of machine learning for big data processing. EURASIP J. Adv. Signal Process. 2016, 67 (2016)","journal-title":"EURASIP J. Adv. Signal Process."},{"key":"182_CR26","unstructured":"Scarlatti, D.: Dart white paper. Tech. rep., DART\u2014Data-Driven Aircraft Trajectory Prediction Research (H2020) (2017). \nhttp:\/\/dart-research.eu\/2017\/04\/03\/dart-white-paper\/"},{"key":"182_CR27","unstructured":"Sip, S., Green, S.: Common trajectory prediction capability for decision support tools. In: ATM 5th USA\/Europa R&D seminar (2003)"},{"key":"182_CR28","doi-asserted-by":"crossref","unstructured":"Song, Y., Cheng, P., Mu, C.: An improved trajectory prediction algorithm based on trajectory data mining for air traffic management. In: Proceedings of the IEEE ICIA 2012 (2012)","DOI":"10.1109\/ICInfA.2012.6246959"},{"key":"182_CR29","doi-asserted-by":"crossref","unstructured":"Tao, Y., Faloutsos, C., Papadias, D., Liu, B.: Prediction and indexing of moving objects with unknown motion patterns. In: Proceedings of the ACM SIGMOD 2004 (2004)","DOI":"10.1145\/1007568.1007637"},{"key":"182_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.trc.2013.11.013","volume":"39","author":"K Tastambekov","year":"2014","unstructured":"Tastambekov, K., Puechmorel, S., Delahaye, D., et al.: Aircraft trajectory forecasting using local functional regression in sobolev space. Transp. Res. Part C Emerg. Technol. 39, 1\u201322 (2014)","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"182_CR31","volume-title":"Pattern Recognition","author":"S Theodoridis","year":"2008","unstructured":"Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 4th edn. Academic Press, Cambridge (2008)","edition":"4"},{"issue":"1","key":"182_CR32","doi-asserted-by":"publisher","first-page":"15","DOI":"10.2514\/1.58508","volume":"36","author":"D Thipphavong","year":"2013","unstructured":"Thipphavong, D., Schultz, C., et al.: Adaptive algorithm to improve trajectory prediction accuracy of climbing aircraft. J. Guid. Control Dyn. (JGCD) 36(1), 15\u201324 (2013)","journal-title":"J. Guid. Control Dyn. (JGCD)"},{"key":"182_CR33","doi-asserted-by":"publisher","first-page":"350","DOI":"10.1016\/j.is.2015.11.002","volume":"64","author":"R Trasarti","year":"2017","unstructured":"Trasarti, R., Guidotti, R., Monreale, A., Giannotti, F.: Myway: location prediction via mobility profiling. Inf. Syst. 64, 350\u2013367 (2017)","journal-title":"Inf. Syst."},{"key":"182_CR34","unstructured":"Vouros, G., Pelekis, N., Kravaris, T., Georgiou, H., et al.: Dart\u2014a machine-learning approach to trajectory prediction and demand-capacity balancing. In: 7th SESAR innovation days (SIDs 2017), SESAR (2017)"},{"key":"182_CR35","unstructured":"Vouros, G., Vlachou, A., Santipantakis, G., Doulkeridis, C., Pelekis, N., Georgiou, H., et al.: Big data analytics for time critical mobility forecasting - recent progress and research challenges. In: 21st international conference on extending database technology (EDBT 2018), ACM (2018)"},{"key":"182_CR36","unstructured":"Vouros, G., Vlachou, A., Santipantakis, G., et al.: Big data analytics for time critical mobility forecasting: recent progress and research challenges. In: Proceedings of the EDBT 2018 (2018)"},{"issue":"3","key":"182_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2743025","volume":"6","author":"Y Zheng","year":"2015","unstructured":"Zheng, Y.: Trajectory data mining: an overview. Trans. Intell. Syst. Technol. 6(3), 1\u201341 (2015)","journal-title":"Trans. Intell. Syst. Technol."}],"container-title":["International Journal of Data Science and Analytics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s41060-019-00182-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s41060-019-00182-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s41060-019-00182-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,3,27]],"date-time":"2020-03-27T00:30:54Z","timestamp":1585269054000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s41060-019-00182-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,3,28]]},"references-count":37,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2020,3]]}},"alternative-id":["182"],"URL":"https:\/\/doi.org\/10.1007\/s41060-019-00182-4","relation":{},"ISSN":["2364-415X","2364-4168"],"issn-type":[{"value":"2364-415X","type":"print"},{"value":"2364-4168","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,3,28]]},"assertion":[{"value":"13 December 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 March 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 March 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}