PhD Defense by Harold Nikoue

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Harold Nikoue
(Co-Advisors: Prof. John-Paul Clarke and Prof. David Goldsman)

will defend a doctoral thesis entitled

Model-based approaches to the utilization of heterogeneous non-overlapping data to dynamically update airport queue estimates


Monday, November 8 at 9:00 a.m.

MK 317

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.


  • Prof. John-Paul Clarke – School of Aerospace Engineering and School of ISyE (Co-advisor)
  • Prof. David Goldsman – School of Industrial and System Engineering (Co-advisor)
  • Prof. Eric Feron – School of Aerospace Engineering
  • Prof. Brian German – School of Aerospace Engineering
  • Prof. Kemal Dinçer Dingeç – Department of Industrial Engineering, Gebze Technical University


  • Workflow Status: Published
  • Created By: Tatianna Richardson
  • Created: 09/22/2021
  • Modified By: Tatianna Richardson
  • Modified: 09/22/2021


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