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Hamsa Balakrishnan. Hanley, Zebulon James Massachusetts Institute of Technology. Operations Research Center. Massachusetts Institute of Technology. Operations Research Center Sloan School of Management n-us--- 2015-09-17T17:42:59Z 2015-09-17T17:42:59Z 2015 2015 http://hdl.handle.net/1721.1/98567 920857787 Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2015. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 101-102). This 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. by Zebulon James Hanley. S.M. 102 pages eng Massachusetts Institute of Technology 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. http://dspace.mit.edu/handle/1721.1/7582 Operations Research Center. Delay characterization and prediction in major U.S. airline networks Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="ff614a7c-d91e-464d-adeb-c7d0246e3054"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Delay characterization and prediction in major U.S. airline networks</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2015</PublicationDate> <Authors> <Author> <DisplayName>Hanley, Zebulon James</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Operations Research Center.</Keyword> <Abstract>This 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.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>