Exploratory data analysis for airline disruption management

• Main Authors: Kolawole Ogunsina, Ilias Bilionis, Daniel DeLaurentis
• Format: Article
• Language: English
• Published: Elsevier 2021-12-01
• Series: Machine Learning with Applications
• Subjects: Airline disruption management; Data analysis; Machine learning
• Online Access: http://www.sciencedirect.com/science/article/pii/S2666827021000517

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.