Review on state of art data mining and machine learning techniques for intelligent Airport systems

Abstract

It is a generally accepted fact that the Airport is the focal point of the country which creates a lasting impression of its people. The challenge faced by airports today is the complexity of players and processes, and the inability of multiple systems to share and analyze data. In order to face this challenge, many airports have implemented isolated solutions. While these solutions may improve specific processes or functions they are not holistic enough. The airport ecosystem must become more `intelligent' to optimize its supply chain, share real-time information, predict certain outcomes and track, manage and locate all of its assets. So the need of the hour is to create a unified, integrated, resourceful and ready to use platform to make intelligent decisions and assist airports to reach its next level. The aim of this paper is to review selected data mining techniques that can be integrated in to such system. Entities such as airlines, airport retails sector and the airport itself is considered for this cause and the data mining techniques that can be applied to these entities to improve the current airport systems such as flight delay prediction, passenger profiling, segmentation, association rule mining are discussed to find better approaches for an intelligent airport system.