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2021 | OriginalPaper | Chapter

Forecasting Air Flight Delays and Enabling Smart Airport Services in Apache Spark

Authors : Gerasimos Vonitsanos, Theodor Panagiotakopoulos, Andreas Kanavos, Athanasios Tsakalidis

Published in: Artificial Intelligence Applications and Innovations. AIAI 2021 IFIP WG 12.5 International Workshops

Publisher: Springer International Publishing

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Abstract

In light of the rapidly growing passenger and flight volumes, airports seek for sustainable solutions to improve passengers’ experience and comfort, while maximizing their profits. A major technological solution towards improving service quality and management processes in airports comprises Internet of Things (IoT) systems that realize the concept of smart airports and offer interconnection potential with other public infrastructures and utilities of smart cities. In order to deliver smart airport services, real-time flight delay data and forecasts are a critical source of information. This paper introduces an essential methodology using machine learning techniques on Apache Spark, a cloud computing framework, with Apache MLlib, a machine learning library to develop and implement prediction models for air flight delays that could be integrated with information systems in order to provide up-to-date analytics. The experimental results have been implemented with various algorithms in terms of classification as well as regression, thus manifesting the potential of the proposed framework.

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Footnotes
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Metadata
Title
Forecasting Air Flight Delays and Enabling Smart Airport Services in Apache Spark
Authors
Gerasimos Vonitsanos
Theodor Panagiotakopoulos
Andreas Kanavos
Athanasios Tsakalidis
Copyright Year
2021
Book
Artificial Intelligence Applications and Innovations. AIAI 2021 IFIP WG 12.5 International Workshops

Print ISBN: 978-3-030-79156-8

Electronic ISBN: 978-3-030-79157-5

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-79157-5_33

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