{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,21]],"date-time":"2026-06-21T22:47:20Z","timestamp":1782082040927,"version":"3.54.5"},"publisher-location":"Cham","reference-count":39,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032294586","type":"print"},{"value":"9783032294593","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-29459-3_16","type":"book-chapter","created":{"date-parts":[[2026,6,21]],"date-time":"2026-06-21T21:53:37Z","timestamp":1782078817000},"page":"221-237","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Human-AI Decision Support for\u00a0Sustainable Air Traffic Controller Workload: Forecast-Informed Bilevel-Optimized Task-Load Estimation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-7242-0898","authenticated-orcid":false,"given":"Mercedes Premalatha","family":"Ramesh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1505-1970","authenticated-orcid":false,"given":"Mengtao","family":"Lyu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0760-0490","authenticated-orcid":false,"given":"Imen","family":"Dhief","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7730-1619","authenticated-orcid":false,"given":"Zhimin","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2889-7222","authenticated-orcid":false,"given":"Mir","family":"Feroskhan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,22]]},"reference":[{"key":"16_CR1","unstructured":"Bai, S., Kolter, J.Z., Koltun, V.: An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. In: International Conference on Learning Representations (ICLR) (2018)"},{"key":"16_CR2","unstructured":"Bloem, M., Gupta, P., Lai, C.F., Kopardekar, P.: Benefits assessment of algorithmically combining generic high altitude airspace sectors. 27th Congress of the International Council of the Aeronautical Sciences (2009)"},{"key":"16_CR3","unstructured":"Chakravarthi, B.R., et\u00a0al.: Dravidianmultimodality: A dataset for multi-modal sentiment analysis in tamil and malayalam. arXiv:2106.04853 (2021). https:\/\/arxiv.org\/abs\/2106.04853"},{"key":"16_CR4","doi-asserted-by":"publisher","unstructured":"Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785\u2013794 (2016). https:\/\/doi.org\/10.1145\/2939672.2939785","DOI":"10.1145\/2939672.2939785"},{"key":"16_CR5","unstructured":"Crutchfield, J., Rosenberg, C.: Predicting subjective workload ratings: A comparison and synthesis of operational and theoretical models. DOT\/FAA\/AM-07\/6 Tech. Rep. FAA Office of Aerospace Medicine (2007)"},{"issue":"1","key":"16_CR6","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1518\/155723408X342826","volume":"3","author":"FT Durso","year":"2008","unstructured":"Durso, F.T., Manning, C.A.: Air traffic control. Rev. Human Factors Ergonomics 3(1), 195\u2013244 (2008). https:\/\/doi.org\/10.1518\/155723408X342826","journal-title":"Rev. Human Factors Ergonomics"},{"key":"16_CR7","unstructured":"EUROCONTROL: Seven-year flight forecast 2025\u20132031 (2025). https:\/\/www.eurocontrol.int\/publication\/eurocontrol-forecast-2025-2031"},{"key":"16_CR8","unstructured":"Federal Aviation Administration: En route decision support tool (edst) description (2025). https:\/\/www.faa.gov\/air_traffic\/publications, Accessed 2025"},{"key":"16_CR9","unstructured":"Federal Aviation Administration: Faa aerospace forecast, fiscal years 2025\u20132045 (2025). https:\/\/www.faa.gov\/data_research\/aviation\/aerospace_forecasts"},{"key":"16_CR10","unstructured":"Gianazza, D.: Learning air traffic controller workload from past sector operations. In: USA\/Europe Air Traffic Management Research and Development Seminar (2017)"},{"key":"16_CR11","doi-asserted-by":"crossref","unstructured":"Guo, H.: Probabilistic load forecasting for integrated energy systems using quantile temporal convolutional networks. Energy Syst. (2024)","DOI":"10.1016\/j.adapen.2024.100165"},{"key":"16_CR12","unstructured":"Hande, A., others, Thamburaj, K.P.: Hope speech detection in under-resourced kannada language. arXiv:2108.04616 (2021). https:\/\/arxiv.org\/abs\/2108.04616"},{"key":"16_CR13","doi-asserted-by":"publisher","unstructured":"Hart, S.G., Staveland, L.E.: Development of nasa-tlx (task load index): Results of empirical and theoretical research. Adv. Psychol. 52, 139\u2013183. Elsevier (1988). https:\/\/doi.org\/10.1016\/S0166-4115(08)62386-9","DOI":"10.1016\/S0166-4115(08)62386-9"},{"key":"16_CR14","volume-title":"Air Traffic Control Decision Support Tool Design and Implementation Handbook","author":"D Herschler","year":"2019","unstructured":"Herschler, D., et al.: Air Traffic Control Decision Support Tool Design and Implementation Handbook. Tech. rep, FAA Human Factors Division (2019)"},{"key":"16_CR15","unstructured":"International Air Transport Association: Global outlook for air transport (2025). https:\/\/www.iata.org\/"},{"key":"16_CR16","unstructured":"International Civil Aviation Organization: Icao strategic plan 2026\u20132050 (2024). https:\/\/www.icao.int\/"},{"key":"16_CR17","unstructured":"International Organization for Standardization: Iso 9241-11: Ergonomics of human-system interaction \u2014 part 11: Usability: Definitions and concepts (2018), International Standard"},{"key":"16_CR18","unstructured":"Jensen, V., Bianchi, F.M., Anfinsen, S.N.: Ensemble conformalized quantile regression for probabilistic time series forecasting, arXiv preprint arXiv:2202.08756 (2022)"},{"issue":"8","key":"16_CR19","doi-asserted-by":"publisher","first-page":"10636","DOI":"10.1109\/TII.2024.3397392","volume":"20","author":"Q Jia","year":"2024","unstructured":"Jia, Q., Xiao, J., Feroskhan, M.: Multitarget assignment under uncertain information through decision support systems. IEEE Trans. Industr. Inf. 20(8), 10636\u201310646 (2024). https:\/\/doi.org\/10.1109\/TII.2024.3397392","journal-title":"IEEE Trans. Industr. Inf."},{"issue":"1","key":"16_CR20","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1207\/S15327566IJCE0401_04","volume":"4","author":"JY Jian","year":"2000","unstructured":"Jian, J.Y., Bisantz, A.M., Drury, C.G.: Foundations for an empirically determined scale of trust in automated systems. Int. J. Cogn. Ergon. 4(1), 53\u201371 (2000). https:\/\/doi.org\/10.1207\/S15327566IJCE0401_04","journal-title":"Int. J. Cogn. Ergon."},{"key":"16_CR21","unstructured":"Kopardekar, P., Magyarits, S.: An algorithmic approach for airspace sector combining. In: USA\/Europe Air Traffic Management Research and Development Seminar (2009)"},{"key":"16_CR22","doi-asserted-by":"publisher","unstructured":"Laskowski, J., et al.: Ai-based method of air traffic controller workload assessment. In: 2024 11th International Workshop on Metrology for AeroSpace (MetroAeroSpace), pp. 46\u201351 (2024). https:\/\/doi.org\/10.1109\/MetroAeroSpace61015.2024.10591524","DOI":"10.1109\/MetroAeroSpace61015.2024.10591524"},{"key":"16_CR23","doi-asserted-by":"crossref","unstructured":"Lemetti, A., Meyer, L., Peukert, M., Polishchuk, T., Schmidt, C., Wylde, H.A.: Eye in the sky: Predicting air traffic controller workload through eye-tracking based machine learning. In: IEEE\/AIAA Digital Avionics Systems Conference (2024)","DOI":"10.59490\/joas.2025.8034"},{"key":"16_CR24","doi-asserted-by":"publisher","unstructured":"Li, Z., Li, F., Lyu, M.: Tracking the unseen and unaware: Deciphering controllers\u2019 detection failures to warnings through eye-tracking metrics. Int. J. Human\u2013Comput. Inter. 1\u201320 (2025). https:\/\/doi.org\/10.1080\/10447318.2024.2448877","DOI":"10.1080\/10447318.2024.2448877"},{"key":"16_CR25","doi-asserted-by":"publisher","unstructured":"Li, Z., Li, Z., Li, F.: Visual attention analytics for individual perception differences and task load-induced inattentional blindness. In: Cross-Cultural Design. HCII 2023. Lecture Notes in Computer Science, pp. 71\u201383. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-35939-2_6","DOI":"10.1007\/978-3-031-35939-2_6"},{"key":"16_CR26","doi-asserted-by":"crossref","unstructured":"Manning, C.A., Mills, S.H., Fox, C., Pfleiderer, E.M., Mogilka, H.J.: Using air traffic control taskload measures and communication events to predict subjective workload. Tech. Rep. DOT\/FAA\/AM-02\/4, FAA Office of Aerospace Medicine (2002)","DOI":"10.1037\/e430162004-001"},{"key":"16_CR27","doi-asserted-by":"publisher","unstructured":"Oktal, H., Yaman, K.: A new approach to air traffic controller workload measurement and modelling. Aircraft Eng. Aerosp. Technol. 83(1), 35\u201342 (2011). https:\/\/doi.org\/10.1108\/00022661111119900","DOI":"10.1108\/00022661111119900"},{"key":"16_CR28","unstructured":"Moreno, P., Zamarre\u00f1o Su\u00e1rez, F., G\u00f3mez Comendador, M., V.F.: Dynamic methodology for air traffic control sector combining. In: International Conference on Research in Air Transportation (2023)"},{"key":"16_CR29","unstructured":"Rad\u00fcntz, T.: Eeg-based psychophysiological assessment of mental workload in air traffic control using the dfhm workload index. Front. Neurosci. (2020)"},{"key":"16_CR30","unstructured":"Roychoudhury, I., Spirkovska, L., O\u2019Connor, M., Kulkarni, C.: Survey of methods to predict controller workload for real-time monitoring of airspace safety. Tech. Rep. NASA\/TM-2018-219985, NASA Ames Research Center (2018)"},{"key":"16_CR31","doi-asserted-by":"publisher","unstructured":"Schmidt, D.K.: A queueing analysis of the air traffic controller\u2019s workload. IEEE Trans. Syst. Man Cybern. SMC. 8(6), 492\u2013498 (1978). https:\/\/doi.org\/10.1109\/TSMC.1978.4309962","DOI":"10.1109\/TSMC.1978.4309962"},{"key":"16_CR32","unstructured":"Transportation Research Board: Air Traffic Controller Staffing in the En Route Domain. Special Report 301, National Academies Press (2010)"},{"issue":"5","key":"16_CR33","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1109\/TSMC.1980.4308343","volume":"10","author":"MK Tulga","year":"1980","unstructured":"Tulga, M.K., Sheridan, T.B.: Dynamic decisions and workload in multitask supervisory control. IEEE Trans. Syst. Man Cybern. 10(5), 217\u2013232 (1980). https:\/\/doi.org\/10.1109\/TSMC.1980.4308343","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"16_CR34","doi-asserted-by":"crossref","unstructured":"Vasseur, C.: A comparison of quantile regression methods for probabilistic load forecasting. Int. J. Forecast. (2021)","DOI":"10.1038\/s41598-021-90063-3"},{"key":"16_CR35","doi-asserted-by":"crossref","unstructured":"Volf, P., Rollo, M.: Airspace sectorization optimization using fast-time simulation of air traffic controller\u2019s workload. In: Proceedings of the 12th USA\/Europe ATM Seminar (2017)","DOI":"10.1109\/ICNSURV.2017.8011949"},{"issue":"7","key":"16_CR36","doi-asserted-by":"publisher","first-page":"7202","DOI":"10.1109\/TVT.2022.3170725","volume":"71","author":"J Xiao","year":"2022","unstructured":"Xiao, J., Feroskhan, M.: Cyber attack detection and isolation for a quadrotor UAV with modified sliding innovation sequences. IEEE Trans. Veh. Technol. 71(7), 7202\u20137214 (2022). https:\/\/doi.org\/10.1109\/TVT.2022.3170725","journal-title":"IEEE Trans. Veh. Technol."},{"key":"16_CR37","doi-asserted-by":"crossref","unstructured":"Yu, X., Chen, C.H., Yang, H.: Cognitive workload quantification for air traffic controllers with ensemble semi-supervised learning. Adv. Eng. Inform. (2025)","DOI":"10.1016\/j.aei.2024.103065"},{"key":"16_CR38","doi-asserted-by":"publisher","unstructured":"Zamarre\u00f1o\u00a0Su\u00e1rez, M., et al.: Methodology for determining the event-based taskload of an air traffic controller using real-time simulations. Aerospace 10(2), 97 (2023). https:\/\/doi.org\/10.3390\/aerospace10020097","DOI":"10.3390\/aerospace10020097"},{"key":"16_CR39","doi-asserted-by":"publisher","unstructured":"Zamarre\u00f1o\u00a0Su\u00e1rez, M., et al.: Understanding the research on air traffic controller workload and its implications for safety: A science mapping-based analysis. Safety Sci. 176, 106545 (2024). https:\/\/doi.org\/10.1016\/j.ssci.2024.106545","DOI":"10.1016\/j.ssci.2024.106545"}],"container-title":["Lecture Notes in Computer Science","Engineering Psychology and Cognitive Ergonomics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-29459-3_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,21]],"date-time":"2026-06-21T21:53:43Z","timestamp":1782078823000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-29459-3_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032294586","9783032294593"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-29459-3_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"22 June 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"HCII","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Human-Computer Interaction","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Montreal, QC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31 July 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"hcii2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2026.hci.international\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}