Airport resource allocation using machine learning techniques
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Asociación Española para la Inteligencia Artificial
2020-05-01
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| Series: | Inteligencia Artificial |
| Online Access: | https://journal.iberamia.org/index.php/intartif/article/view/381 |
| _version_ | 1819241460420050944 |
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| author | Maged Mamdouh Mostafa Ezzat Hesham A. Hefny |
| author_facet | Maged Mamdouh Mostafa Ezzat Hesham A. Hefny |
| author_sort | Maged Mamdouh |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-23T14:24:16Z |
| format | Article |
| id | doaj.art-8612037f7147473d8654cdb403461ae3 |
| institution | Directory Open Access Journal |
| issn | 1137-3601 1988-3064 |
| language | English |
| last_indexed | 2024-12-23T14:24:16Z |
| publishDate | 2020-05-01 |
| publisher | Asociación Española para la Inteligencia Artificial |
| record_format | Article |
| series | Inteligencia Artificial |
| spelling | doaj.art-8612037f7147473d8654cdb403461ae32022-12-21T17:43:43ZengAsociación Española para la Inteligencia ArtificialInteligencia Artificial1137-36011988-30642020-05-01236510.4114/intartif.vol23iss65pp19-32Airport resource allocation using machine learning techniquesMaged Mamdouh0Mostafa EzzatHesham A. HefnyDepartment of Computer Science Faculty of Graduate Studies for statistical Researches Cairo University, EgyptThe 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.https://journal.iberamia.org/index.php/intartif/article/view/381 |
| spellingShingle | Maged Mamdouh Mostafa Ezzat Hesham A. Hefny Airport resource allocation using machine learning techniques Inteligencia Artificial |
| title | Airport resource allocation using machine learning techniques |
| title_full | Airport resource allocation using machine learning techniques |
| title_fullStr | Airport resource allocation using machine learning techniques |
| title_full_unstemmed | Airport resource allocation using machine learning techniques |
| title_short | Airport resource allocation using machine learning techniques |
| title_sort | airport resource allocation using machine learning techniques |
| url | https://journal.iberamia.org/index.php/intartif/article/view/381 |
| work_keys_str_mv | AT magedmamdouh airportresourceallocationusingmachinelearningtechniques AT mostafaezzat airportresourceallocationusingmachinelearningtechniques AT heshamahefny airportresourceallocationusingmachinelearningtechniques |