TY - GEN
T1 - Forecasting Air Flight Delays and Enabling Smart Airport Services in Apache Spark
AU - Vonitsanos, Gerasimos
AU - Panagiotakopoulos, Theodor
AU - Kanavos, Andreas
AU - Tsakalidis, Athanasios
N1 - Funding Information:
Acknowledgement. Supported by the Erasmus+ KA2 under the project DEVOPS, “DevOps competences for Smart Cities” (Project No.: 601015-EPP-1-2018-1-EL-EPPKA2-SSA Erasmus+ Program, KA2: Cooperation for innovation and the exchange of good practices-Sector Skills Alliances, started in 2019, January 1).
Publisher Copyright:
© 2021, IFIP International Federation for Information Processing.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Air flight delays forecasting
KW - Apache spark
KW - Classification
KW - Machine learning
KW - Regression
KW - Smart airports
KW - Smart cities
UR - http://www.scopus.com/inward/record.url?scp=85112638561&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-79157-5_33
DO - 10.1007/978-3-030-79157-5_33
M3 - Conference contribution
AN - SCOPUS:85112638561
SN - 9783030791568
T3 - IFIP Advances in Information and Communication Technology
SP - 407
EP - 417
BT - Artificial Intelligence Applications and Innovations. AIAI 2021 IFIP WG 12.5 International Workshops - 5G-PINE 2021, AI-BIO 2021, DAAI 2021, DARE 2021, EEAI 2021, and MHDW 2021, Proceedings
A2 - Maglogiannis, Ilias
A2 - Macintyre, John
A2 - Iliadis, Lazaros
PB - Springer Science and Business Media Deutschland GmbH
T2 - 17th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2021, 6th Workshop on 5G-Putting Intelligence to the Network Edge, 5G-PINE 2021, Artificial Intelligence in Biomedical Engineering and Informatics Workshop, AI-BIO 2021, Workshop on Defense Applications of AI, DAAI 2021, Distributed AI for Resource-Constrained Platforms Workshop, DARE 2021, Energy Efficiency and Artificial Intelligence Workshop, EEAI 2021, and 10th Mining Humanistic Data Workshop, MHDW 2021
Y2 - 25 June 2021 through 27 June 2021
ER -
