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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Improving messaging to airport community residents: An application of sentiment analysis to community engagement
(Henry Stewart Talks Ltd, 2021-06-01)Natural Language Processing has made significant progress over the last decades with the development of open-source software and dedicated libraries. Sentiment analysis has become a best practice in both private and public ... -
Connecting Pedestrians with Disabilities to Adaptive Signal Control for Safe Intersection Crossing and Enhanced Mobility
(National Transportation Library, 2018-08)This report summarizes work performed undercontract #DTFH6117C0001 toward the development of PedPal, a prototype mobile (smartphone) app designed to assist pedestrians with disabilities when crossing the street at signalized ... -
Vulnerable Road User Detection Using Smartphone Sensors and Recurrence Quantification Analysis
(IEEE, 2019-11-28)With the fast advancements of the Autonomous Vehicle (AV) industry, detection of Vulnerable Road Users (VRUs) using smartphones is critical for safety applications of Cooperative Intelligent Transportation Systems (C-ITSs). ... -
Ethics, Data Science, and Health and Human Services: Embedded Bias in Policy Approaches to Teen Pregnancy Prevention
(2020-06-07)Background: This study aims to evaluate the Chicago Teen Pregnancy Prevention Initiative delivery optimization outcomes given policy-neutral and policy-focused approaches to deliver this program to at-risk teens across the ... -
Developing a Novel Crowdsourcing Business Model for Micro-Mobility Ride-Sharing Systems: Methodology and Preliminary Results
(2020-07-30)Micro-mobility ride-sharing is an emerging technology that provides access to the transit system with minimum environmental impacts. Significant research is required to ensure that micro-mobility ride-sharing provides a ... -
Deep Transfer Learning for Vulnerable Road Users Detection using Smartphone Sensors Data
(MDPI, 2020-10-25)As the Autonomous Vehicle (AV) industry is rapidly advancing, the classification of non-motorized (vulnerable) road users (VRUs) becomes essential to ensure their safety and to smooth operation of road applications. The ... -
Perception Analysis of E-Scooter Riders and Non-Riders in Riyadh, Saudi Arabia: Survey Outputs
(MDPI, 2021-01-16)This study explores the feasibility of launching an e-scooter sharing system as a new micro-mobility mode, and part of the public transportation system in the city of Riyadh, Saudi Arabia. Therefore, survey was conducted ... -
Joint Impact of Rain and Incidents on Traffic Stream Speeds
(Hindawi, 2021-01-11)Unpredictable and heterogeneous weather conditions and road incidents are common factors that impact highway traffic speeds. A better understanding of the interplay of different factors that affect roadway traffic speeds ... -
Impact of risk factors on work zone crashes using logistic models and Random Forest
(2021-04-14)Work zone safety is influenced by many risk factors. Consequently, a comprehensive knowledge of the risk factors identified from crash data analysis becomes critical in reducing risk levels and preventing severe crashes ... -
A Novel Crowdsourcing Model for Micro-Mobility Ride-Sharing Systems
(MDPI, 2021-07-06)Substantial research is required to ensure that micro-mobility ride sharing provides a better fulfilment of user needs. This study proposes a novel crowdsourcing model for the ride-sharing system where light vehicles such ... -
Evaluation of the Use of a Road Diet Design: An Urban Corridor Case Study in Washington, DC
(MDPI, 2021-08-11)A traditional road diet design converts a four-lane two-way road to a three-lane road consisting of two through lanes and a center two-way left turn lane. This paper introduces a new application of the road diet design in ... -
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 ... -
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 ...
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