Airport resource allocation using machine learning techniques

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      <subfield code="a">Mamdouh, Maged </subfield>
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      <subfield code="c">Maged Mamdouh, Mostafa Ezzat, Hesham A.Hefny</subfield>
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      <subfield code="a">The airport ground handling has a global trend to meet the Service Level Agreement (SLA) requirements that represents resource allocation with more restrictions according to flights. That can be achieved by predicting future resource allocation. This research presents a comparison between the most used machine learning techniques implemented in many different elds for resource allocation and demand prediction. The prediction model nominated and used in this research is the Support Vector Machine (SVM) to predict the required resources for each  flight, despite the restrictions imposed by airlines when contracting their services in the SLA. The approach has been trained and tested using real data from Cairo international airport. the proposed SVM technique implemented and explained with a varying accuracy of resource allocation prediction, showing that even for variations accuracy in resource prediction in different scenarios, the SVM technique can produce a good performance as resource allocation in the airport.</subfield>
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      <subfield code="a">Hefny, Hesham A.</subfield>
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      <subfield code="t">Revista Iberoamericana de Inteligencia Artificial</subfield>
      <subfield code="d">IBERAMIA, Sociedad Iberoamericana de Inteligencia Artificial , 2018-</subfield>
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      <subfield code="g">01/06/2020 Volumen 23 Número 65 - junio 2020 , p. 19-32</subfield>
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