Exploratory data analysis for airline disruption management
Reliable platforms for data collation during airline schedule operations have significantly increased the quality and quantity of available information for effectively managing airline schedule disruptions. To that effect, this paper applies macroscopic and microscopic techniques by way of basic sta...
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2021-12-01
|
Series: | Machine Learning with Applications |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2666827021000517 |
id |
doaj-e009e775f40842ed8dc756bd5cc10eba |
---|---|
record_format |
Article |
spelling |
doaj-e009e775f40842ed8dc756bd5cc10eba2021-07-11T04:29:23ZengElsevierMachine Learning with Applications2666-82702021-12-016100102Exploratory data analysis for airline disruption managementKolawole Ogunsina0Ilias Bilionis1Daniel DeLaurentis2School of Aeronautics and Astronautics, Purdue University, United States; Corresponding author.Department of Mechanical Engineering, Purdue University, United StatesSchool of Aeronautics and Astronautics, Purdue University, United StatesReliable platforms for data collation during airline schedule operations have significantly increased the quality and quantity of available information for effectively managing airline schedule disruptions. To that effect, this paper applies macroscopic and microscopic techniques by way of basic statistics and machine learning, respectively, to analyze historical scheduling and operations data from a major airline in the United States. Macroscopic results reveal that majority of irregular operations in airline schedule that occurred over a one-year period stemmed from disruptions due to flight delays, while microscopic results validate different modeling assumptions about key drivers for airline disruption management like turnaround as a Gaussian process.http://www.sciencedirect.com/science/article/pii/S2666827021000517Airline disruption managementData analysisMachine learning |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Kolawole Ogunsina Ilias Bilionis Daniel DeLaurentis |
spellingShingle |
Kolawole Ogunsina Ilias Bilionis Daniel DeLaurentis Exploratory data analysis for airline disruption management Machine Learning with Applications Airline disruption management Data analysis Machine learning |
author_facet |
Kolawole Ogunsina Ilias Bilionis Daniel DeLaurentis |
author_sort |
Kolawole Ogunsina |
title |
Exploratory data analysis for airline disruption management |
title_short |
Exploratory data analysis for airline disruption management |
title_full |
Exploratory data analysis for airline disruption management |
title_fullStr |
Exploratory data analysis for airline disruption management |
title_full_unstemmed |
Exploratory data analysis for airline disruption management |
title_sort |
exploratory data analysis for airline disruption management |
publisher |
Elsevier |
series |
Machine Learning with Applications |
issn |
2666-8270 |
publishDate |
2021-12-01 |
description |
Reliable platforms for data collation during airline schedule operations have significantly increased the quality and quantity of available information for effectively managing airline schedule disruptions. To that effect, this paper applies macroscopic and microscopic techniques by way of basic statistics and machine learning, respectively, to analyze historical scheduling and operations data from a major airline in the United States. Macroscopic results reveal that majority of irregular operations in airline schedule that occurred over a one-year period stemmed from disruptions due to flight delays, while microscopic results validate different modeling assumptions about key drivers for airline disruption management like turnaround as a Gaussian process. |
topic |
Airline disruption management Data analysis Machine learning |
url |
http://www.sciencedirect.com/science/article/pii/S2666827021000517 |
work_keys_str_mv |
AT kolawoleogunsina exploratorydataanalysisforairlinedisruptionmanagement AT iliasbilionis exploratorydataanalysisforairlinedisruptionmanagement AT danieldelaurentis exploratorydataanalysisforairlinedisruptionmanagement |
_version_ |
1721309534809489408 |