UMBC Data Science
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The Data Science graduate program at UMBC prepares students to respond to the growing demand for professionals with data science knowledge, skills, and abilities. Our program brings together faculty from a wide range of fields who have a deep understanding of the real-world applications of data analytics. UMBC’s Data Science programs prepare students to excel in data science roles through hands-on experience, rigorous academics, and access to a robust network of knowledgeable industry professionals.
Recent Submissions
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Predicting Block Time: An Application of Quantile Regression
(IntechOpen, 2012-08-01) -
Can machines learn how to forecast taxi-out time? A comparison of predictive models applied to the case of Seattle/Tacoma International Airport
(Elsevier, 2018-10-15)This study compares the performance of ensemble machine learning, ordinary least-squared and penalized algorithms to predict taxi-out time at two different periods of NextGen capability implementation. In the pre-sample, ... -
An evaluation of the impact of wake vortex re-categorization: The case of Charlotte Douglas International airport (CLT)
(Elsevier, 2018-02-04)This study compared departures before and after wake recat implementation at CLT. In both periods, departure counts and departure demand loaded highly onto Factor 1, arrival demand and gate departure delays onto Factor 2, ... -
Has market concentration fostered on-time performance? A case study of seventy-two U.S. airports
(Elsevier, 2016-09-23)The study compares a multivariate with a quantile regression model to determine whether utilized airport capacity, departure and airborne delays, departure and arrival demand, and market structure explained variations in ... -
An evaluation of departure throughputs before and after the implementation of wake vortex recategorization at Atlanta Hartsfield/Jackson International Airport: A Markov regime-switching approach
(Elsevier, 2015-11-04)This paper utilizes a Markov regime-switching model to decompose airport departures into two regimes and to investigate the change in departure throughputs before and after implementing wake recat at ATL. Although analysts ... -
Is access to general aviation airports with precision approach and no instrument landing systems a club good? A study of six airports
(Elsevier, 2015-05-08)Access to general aviation (GA) airports can generally be viewed as non-excludable and non-rivalrous. However, access to GA airports using lateral vertical guidance (LPV/LP) and no instrument landing systems (ILS) will ... -
Measuring Performance at a Large Metropolitan Area: The Case of the DC (District of Columbia) Metroplex
(Oregon State University, 2014-08)Hierarchical linear models improve the measurement of performance when applied to a construct such as a metroplex. It comparesthe outcomes of a hierarchical linear model with those of a multiple regression model to ... -
Measuring the impact of traffic flow management on interarrival duration: An application of autoregressive conditional duration
(Elsevier, 2014-11-17)The Federal Aviation Administration has several tools in its arsenal to manage traffic flows. However, it is very difficult to assess with certainty the impact of traffic flow management procedures such as Time-Based Flow ... -
Validating delay constructs: An application of confirmatory factor analysis
(Elsevier, 2013-12-21)This paper proposes to use confirmatory factor analysis (CFA) to evaluate the relationship between six observed variables (arrival and departure counts, arrival and departure demand, taxi-out and airborne delays) and their ... -
Measuring Demand for Access to Regional Airports: An Application of Zero-Inflated Poisson Regression
(Scholarly Commons, 2013-03)The demand for access to regional airports is measured by the counts of area navigation (RNA V)/required navigation (RNP) procedures filed in the flight plan and collected by the Federal Aviation Administration's host ... -
An application of survival and frailty analysis to the study of taxi-out time: A case of New York Kennedy Airport
(Elsevier, 2012-11-02)This study uses survival models to evaluate how selected operational factors affect the duration of aircraft taxi-out times at John F. Kennedy Airport, New York. Frailty models help assess whether fixed or random effects ... -
Predicting Block Time: An Application of Quantile Regression
(AgEcon, 2012-08)Airlines face three types of delay that make it difficult to build robust schedules and to support block time predictability. Block time is the time elapsed from gate departure to gate arrival and refers to the time when ... -
Improving schedule reliability based on copulas: An application to five of the most congested US airports
(Elsevier, 2011-04-01)This study, based Newark Liberty International, New York John F. Kennedy, New York LaGuardia, Chicago O’Hare International and San Francisco International in the US, provides a perspective on the predictability of on-time ... -
Can we explain airport performance? A case study of selected New York airports using a stochastic frontier model
(Elsevier, 2010-04-01)This paper uses stochastic frontier models to assess whether technical efficiency at Newark Liberty International, New York John F. Kennedy International, and New York LaGuardia airports improved from June to August 2008 ... -
Do market-concentrated airports propagate more delays than less concentrated ones? A case study of selected U.S. airports
(Elsevier, 2009-01-20)Airport congestion and widespread passenger discontent with airlines' poor on-time performance have recently led the Federal government to reduce peak-time operations at large airports such as Chicago O'Hare and New York ...
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