Browsing by Author "Becker, Andrew"

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    Clustering Methods for Correlated Data
    (2022-08) Becker, Andrew
    Hierarchical Clustering is one of the most popular unsupervised clustering methods.Using a simple agglomerative algorithm, it iteratively combines similar clusters together forming cohesive groups of observations. This work focuses on Hierarchical Clustering and how it may be adapted to accommodate correlated observations. Chapter 2 investigates how to develop a statistical framework for Hierarchical Clustering so we may derive statistical properties from the clustering method. In Chapter 3, a new method, Hierarchical Cohesion Clustering is proposed. This method is a modification of the traditional methods which aims to accommodate correlated observations. This approach explores how repeated measurements may be preprocessed into intermediate clusters to improve clustering outcomes. The method is applied to a sequence-based time use dataset about how people spend their time throughout the day. In Chapter 4, we focus on how to incorporate spatial adjacency data when clustering. We continue to investigate Hierarchical Clustering methods, with a special focus on Hierarchical Cohesion Clustering. Applying the collection of methods to COVID-19 case rate data within counties, a comparison of the methods is performed with summaries of their respective strengths and weaknesses. Spatial simulations are included to better determine each approach’s efficacy and when certain approaches are preferable.
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    COVID-19 Implications on Public Transportation: Understanding Post-Pandemic Transportation Needs, Behaviors, and Experiences
    (Center for Transportation Studies, University of Minnesota, 2022-11) Fan, Yingling; Becker, Andrew; Ryan, Galen; Wolfson, Julian
    The COVID-19 pandemic and widespread social distancing measures have dramatically reduced public transit ridership, leaving transit agencies with massive revenue shortfalls, and it is still unclear how long it will take for transit to recover and whether transit will emerge fundamentally transformed for better or worse after the pandemic. This research collected first-hand data on people's post-pandemic travel behavior decision-making process in the Twin Cities metropolitan region between March and June 2021. Participants were recruited through various forms of digital marketing tools such as a website, social media, emails, and online videos. Of the 339 participants who were enrolled in the study, 154 (45%) used a smartphone app to capture daily transportation needs, behaviors, and experiences for two consecutive weeks. The data provided insights into how the COVID-19 pandemic has shaped people?s attitudes, perceptions, and decisions toward various transportation services, including public transportation, and how the mobility impacts of COVID-19 differ by individual socio-demographics and trip environments. Results from this research will help transportation planners identify innovative and sensible ways to effectively promote the use of public transportation in the post-pandemic era.