Title:
MODEL-BASED APPROACH TO THE UTILIZATION OF HETEROGENEOUS NON-OVERLAPPING DATA IN THE OPTIMIZATION OF COMPLEX AIRPORT SYSTEMS

dc.contributor.advisor Clarke, John-Paul B.
dc.contributor.advisor Goldsman, David
dc.contributor.author Nikoue, Harold
dc.contributor.committeeMember Feron, Eric M
dc.contributor.committeeMember German, Brian
dc.contributor.committeeMember Dinçer Dingeç, Kemal
dc.contributor.department Aerospace Engineering
dc.date.accessioned 2022-01-14T16:12:40Z
dc.date.available 2022-01-14T16:12:40Z
dc.date.created 2021-12
dc.date.issued 2021-12-15
dc.date.submitted December 2021
dc.date.updated 2022-01-14T16:12:40Z
dc.description.abstract Simulation and optimization have been widely used in air transportation, particularly when it comes to determining how flight operations might evolve. However, with regards to passengers and the services provided to them, this is not the case in large part because the data required for such analysis is harder to collect, requiring the timely use of surveys and significant human labor. The ubiquity of always--connected smart devices and the rise of inexpensive smart devices has made it possible to continuously collect passenger information for passenger-centric solutions such as the automatic mitigation of passenger traffic. Using these devices, it is possible to capture dwell times, transit times, and delays directly from the customers. The data; however, is often sparse and heterogeneous, both spatially and temporally. For instance, the observations come at different times and have different levels of accuracy depending on the location, making it challenging to develop a precise network model of airport operations. The objective of this research is to provide online methods to sequentially correct the estimates of the dynamics of a system of queues despite noisy, quickly changing, and incomplete information. First, a sequential change point detection scheme based on a generalized likelihood ratio test is developed to detect a change in the dynamics of a single queue by using a combination of waiting times, time spent in queue, and queue-length measurements. A trade-off is made between the accuracy of the tests, the speed of the tests, the costs of the tests, and the value of utilizing the observations jointly or separately. The contribution is a robust detection methodology that quickly detects a change in queue dynamics from correlated measurements. In the second part of the work, a model-based estimation tool is developed to update the service rate distribution for a single queue from sparse and noisy airport operations data. Model Reference Adaptive Sampling is used in-the-loop to update a generalized gamma distribution for the service rates within a simulation of the queue at an airport’s immigration center. The contribution is a model predictive tool to optimize the service rates based on waiting time information. The two frameworks allow for the analysis of heterogeneous passenger data sources to enable the tactical mitigation of airport passenger traffic delays.
dc.description.degree Ph.D.
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/66157
dc.language.iso en_US
dc.publisher Georgia Institute of Technology
dc.subject Queueing
dc.subject simulation
dc.subject Model-based search
dc.subject Change point detection
dc.subject twin model
dc.subject Multiple hypothesis tests
dc.subject parallel tests
dc.title MODEL-BASED APPROACH TO THE UTILIZATION OF HETEROGENEOUS NON-OVERLAPPING DATA IN THE OPTIMIZATION OF COMPLEX AIRPORT SYSTEMS
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.advisor Goldsman, David
local.contributor.corporatename College of Engineering
local.contributor.corporatename Daniel Guggenheim School of Aerospace Engineering
local.relation.ispartofseries Doctor of Philosophy with a Major in Aerospace Engineering
relation.isAdvisorOfPublication e34280e7-a117-44ac-ac35-2141fe0b4dd1
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
relation.isOrgUnitOfPublication a348b767-ea7e-4789-af1f-1f1d5925fb65
relation.isSeriesOfPublication f6a932db-1cde-43b5-bcab-bf573da55ed6
thesis.degree.level Doctoral
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
NIKOUE-DISSERTATION-2021.pdf
Size:
7.73 MB
Format:
Adobe Portable Document Format
Description:
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
LICENSE.txt
Size:
3.87 KB
Format:
Plain Text
Description: