Massachusetts Institute of Technology
Delay characterization and prediction in major U.S. airline networks
Abstract
dc:description.abstractThis thesis expands on models that predict delays within the National Airspace System (NAS) in the United States. We propose a new method to predict the expected behavior of the NAS throughout the course of an entire day after only a few flying hours have elapsed. We do so by using k-means clustering to classify the daily NAS behavior into a small set of most commonly seen snapshots. We then use random forests to map the delay behavior experienced early in a day to the most similar NAS snapshot, from which we make our type-of-day prediction for the NAS. By noon EST, we are able to predict the NAS type-of-day with 85% accuracy. We then incorporate these NAS type-of-day predictions into previously proposed models to predict the delay on specific origin-destination (OD) pairs within the U.S. at a certain number of hours into the future. The predictions use local delay variables, such as the current delay on specific OD pairs and airports, as well network-level variables such as the NAS type-of-day. These OD pair delay prediction models use random forests to make classification and regression predictions. The effects of changes in classification threshold, prediction horizon, NAS type-of-day inclusion, and using wheel off/on, actual, and scheduled gate departure and arrival times are studied. Lastly, we explore how the delay behavior of the NAS has changed over the last ten years and how well the models perform on new data.
Degree
thesis:*- Department dc:contributor.department
- Massachusetts Institute of Technology. Operations Research Center.
- Grantor dc:publisher
- Massachusetts Institute of Technology
- Year dc:date.issued
- 2015
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Hanley, Zebulon James
- Advisor dc:contributor.advisor
-
- Hamsa Balakrishnan.
Subjects
dc:subject × 1Rights
dc:rights- Statement dc:rights
-
- M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
- Licence dc:rights.uri
- Language dc:language.iso
- eng
Identifiers
dc:identifier.*- Handle dc:identifier.uri
- http://hdl.handle.net/1721.1/98567
- OAI identifier oai:identifier
- oai:dspace.mit.edu:1721.1/98567