Browsing by Author "Alsalous, Osama"
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- Airport Performance Metrics Analysis: Application to Terminal Airspace, Deicing, and ThroughputAlsalous, Osama (Virginia Tech, 2022-06-08)The Federal Aviation Administration (FAA) is continuously assessing the operational performance of the National Airspace System (NAS), where they analyze trends in the aviation industry to help develop strategies for a more efficient air transportation system. To measure the performance of various elements of the aviation system, the FAA and the International Civil Aviation Organization (ICAO) developed nineteen key performance indicators (KPIs). This dissertation contains three research studies, each written in journal format, addressing select KPIs. These studies aim at answering questions that help understand and improve different aspects of airport operational efficiency. In the first study, we model the flight times within the terminal airspace and compare our results with the baseline methodology that the FAA uses for benchmarking. In the second study, we analyze the efficiency of deicing operations at Chicago O'Hare (ORD) by developing an algorithm that analyzes radar data. We also use a simulation model to calculate potential improvements in the deicing operations. Lastly, we present our results of a clustering analysis surrounding the response of airports to demand and capacity changes during the COVID-19 pandemic. The findings of these studies add to literature by providing a methodology that predicts travel times within the last 100 nautical miles with greater accuracy, by providing deicing times per aircraft type, and by providing insight into factors related to airport response to shock events. These findings will be useful for air traffic management decision makers in addition to other researchers in related future studies and airport simulations.
- Airport Scheduling and Operational Performance: A Clustering Analysis of Airport Response to COVID-19Alsalous, Osama; Hotle, Susan (American Institute of Aeronautics and Astronautics, 2023-06)In early 2020, the Coronavirus disease 2019 (COVID-19) pandemic started and forced air travel demand to decrease sharply in most parts of the world due to travel restrictions that were put in place to limit the spread of the virus. The pandemic also impacted capacity due to reasons such as workforce social distancing, days when Air Traffic Control (ATC) facilities were shut down due to COVID cases, and financial challenges due to the decreased demand. The reduced demand created a unique challenge in the system since capacity exceeded demand by very large margins in the NAS, however, delays in the system did not fall to zero despite the sharp drop in demand. This study analyzed operations at 77 United States (US) airports to compare and contrast their responses to the COVID-19 pandemic in terms of capacity, throughput, and the resulting operational performance. We evaluate the response of airports to the initial shock event during 2020 in addition to the recovery period that followed in 2021. The data showed a 67% decline in total operations at the lowest point during the pandemic. The impact during the shock time period varied greatly across the airports, ranging from a reduction of 14.8% at MEM to 81.5% at LGA. We performed a clustering analysis to study airports’ response to the COVID-19 pandemic. There was a number of airport characteristics that were correlated to the changes in airport metrics. For example, the data showed that being located in a multi-airport city was significantly correlated to the decrease in operations during the shock, however, it was not significant in the recovery trends. Our analysis showed that delays in the system did not change proportionately to the change in operations. Similarly, there were only minor improvements in punctuality, on-time flights at the ASPM 77 airports increased by 9.5% while operations declined by 52% during the shock event time period compared to pre-COVID. Part of this phenomenon was a result of schedule peaking which caused delays due to creating busy hours at the airports. This analysis can inform airport management when responding to future disruptive events, it provides insight into airport operational resiliency, response to disruption, and demand recovery patterns based on airport characteristics.
- Global Demand Forecast ModelAlsalous, Osama (Virginia Tech, 2016-01-19)Air transportation demand forecasting is a core element in aviation planning and policy decision making. NASA Langley Research Center addressed the need of a global forecast model to be integrated into the Transportation Systems Analysis Model (TSAM) to fulfil the vision of the Aeronautics Research Mission Directorate (ARMD) at NASA Headquarters to develop a picture of future demand worldwide. Future forecasts can be performed using a range of techniques depending on the data available and the scope of the forecast. Causal models are widely used as a forecasting tool by looking for relationships between historical demand and variables such as economic and population growth. The Global Demand Model is an econometric regression model that predicts the number of air passenger seats worldwide using the Gross Domestic Product (GDP), population, and airlines market share as the explanatory variables. GDP and Population are converted to 2.5 arc minute individual cell resolution and calculated at the airport level in the geographic area 60 nautical miles around the airport. The global demand model consists of a family of models, each airport is assigned the model that best fits the historical data. The assignment of the model is conducted through an algorithm that uses the R2 as the measure of Goodness-of-Fit in addition to a sanity check for the generated forecasts. The output of the model is the projection of the number of seats offered at each airport for every year up to the year 2040.
- Modeling Arrival Flight Times within the Terminal AirspaceAlsalous, Osama; Hotle, Susan (Sage, 2021-05-10)Air traffic management efficiency in the descent phase of flights is a key area of interest in aviation research for the United States, Europe, and recently other parts of the world. The efficiency of arrival travel times within the terminal airspace is one of nineteen key performance indicators defined by the Federal Aviation Administration (FAA) and the International Civil Aviation Organization, typically within 100 nmi of arrival airports. This study models the relationship between travel time within the terminal airspace and contributing factors using a multivariate log-linear model to quantify the impact that these factors have on the total travel time within the last 100 nmi. The results were compared with the baseline set of variables that are currently used for benchmarking at the FAA. The analyzed data included flight and weather data from January 1, 2018 to March 31, 2018 for five airports in the United States: Chicago O’Hare International Airport, Hartsfield-Jackson Atlanta International, San Francisco International Airport, John F. Kennedy International Airport, and LaGuardia Airport. The modeling results showed that there is a significant improvement in prediction accuracy of travel times compared with the baseline methodology when additional factors, such as wind, meteorological conditions, demand and capacity, ground delay programs, market distance, time of day, and day of week, are included. Root mean squared error values from out-of-sample testing were used to measure the accuracy of the estimated models.