Airport resource allocation using machine learning techniques

Maged Mamdouh

doi:10.4114/intartif.vol23iss65pp19-32

Airport resource allocation using machine learning techniques

2020-05-15

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AI

DOI：10.4114/intartif.vol23iss65pp19-32

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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 fields 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.

Keywords:

Ground handling agents

Service level agreement

Resource allocation

Machine learning

Support vector machine

Airport resource allocation using machine learning techniques

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