文章基本信息

标题：Airport resource allocation using machine learning techniques
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作者：Maged Mamdouh ; Mostafa Ezzat ; Hesham A. Hefny 等
期刊名称：Inteligencia Artificial : Ibero-American Journal of Artificial Intelligence
印刷版ISSN：1137-3601
电子版ISSN：1988-3064
出版年度：2020
卷号：23
期号：65
页码：19-32
DOI：10.4114/intartif.vol23iss65pp19-32
出版社：Spanish Association for Intelligence Artificial
摘要：The airport ground handling has a global trend to meet the Service Level Agreement (SLA) requirementsthat represents resource allocation with more restrictions according to flights. That can be achieved by predictingfuture resources demands. this research presents a comparison between the most used machine learning techniquesimplemented in many different fields for demand prediction and resource allocation. The prediction model nomi-nated and used in this research is the Support Vector Machine (SVM) to predict the required resources for eachflight, despite the restrictions imposed by airlines when contracting their services in the Service Level Agreement.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 thateven for variations accuracy in resource prediction in different scenarios; the Support Vector Machine techniquecan produce a good performance as resource allocation in the airport. Download data is not yet available.
关键词：Ground handling agents;Service level agreement ;Resource allocation ;Machine learning ;Support vector machine.