Applications of Business Analytics in Predicting Flight On‐time Performance in a Complex and Dynamic System

Dothang Truong, Mark A. Friend and Hongyun Chen

Transportation Journal, 2018, vol. 57, issue 1, 24-52

Abstract: Flight on‐time performance is one of the most important issues in the National Airspace System, a very complex and dynamic system. To avoid negative impacts to the aviation industry, the Federal Aviation Administration has set a long‐term objective of understanding and mitigating flight delays. Building an effective and accurate prediction model of flight‐delay incidents will help airport executives make the best decisions in delay scenarios. This article utilized two advanced prediction methods to predict the probability of a flight‐delay incident—data mining using the decision tree and data mining using Bayesian inference. Prediction models were built using flight on‐time performance data collected from different sources. The results indicated important airport‐related factors and their effects on the flight on‐time performance.

Date: 2018
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.5325/transportationj.57.1.0024

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:transj:v:57:y:2018:i:1:p:24-52

Access Statistics for this article

More articles in Transportation Journal from John Wiley & Sons
Bibliographic data for series maintained by Wiley Content Delivery ().