Exploratory Data Analysis for Airline Disruption Management
This research provides insights into the causes of airline schedule disruptions for airline operations managers, helping them to better manage irregular operations.
This paper analyzes historical scheduling and operations data from a major US airline over a one-year period. Macroscopic analysis revealed that most irregular operations were due to flight delays, while microscopic analysis validated modeling assumptions about key drivers like turnaround time as a Gaussian process.
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.