Prediction of Gate In Time of Scheduled Flights and Schedule Conformance using Machine Learning-based Algorithms

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dc.contributor.authorSahadevan, Deepudev
dc.contributor.authorPalanisamy, P
dc.contributor.authorGopi, Varun P.
dc.contributor.authorNelli, Manjunath K.
dc.contributor.authorAsok kumar, A.K.
dc.date.accessioned2026-10-30T11:16:29Z
dc.date.available2026-10-30T11:16:29Z
dc.date.issued2020-11-18
dc.description.abstractOver the past several years, the air transportation system has encountered frequent rises in air traffic demand, especially with the introduction of budget airlines. In recent Indian-aviation submits, IATA has presented that by 2026 India is expected to be the third-largest air transport market in the world (International Air Transport Association, 2018) from its current 7th place. Before pandemic COVID-19, increases in air traffic in trend were predicted such that the current air traffic system will not be able to cater the projected air traffic demand of the near future due to the capacity constraints of airport and airspace. In order to overcome this backdrop, there arises a need for the of modernising the air transportation system. Prompt initiatives are being taken by several countries, including India, to develop the future air transport system that will be more robust, predictable, and reliable than today’s one. Introduction of Central Air Traffic flow management(C-ATFM) in India was one of the steps taken towards achieving this objective.
dc.identifier.issn23746793
dc.identifier.urihttps://doi.org/10.15394/ijaaa.2020.1521
dc.identifier.urihttp://nitt.ndl.gov.in/handle/123456789/370
dc.language.isoen
dc.publisherEmbry-Riddle Aeronautical University
dc.relation.ispartofseriesInternational Journal of Aviation, Aeronautics, and Aerospace; Vol. 7/ No. 4
dc.titlePrediction of Gate In Time of Scheduled Flights and Schedule Conformance using Machine Learning-based Algorithms
dc.typeArticle
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