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Preparing small urban areas for shared mobility with autonomous vehicles: a case study of college town

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dc.contributorJones, Steven SJ
dc.contributorHainen, Alexander AH
dc.contributorSmith, Randy RS
dc.contributorGraettinger, Andrew AG
dc.contributor.advisorLiu, Jun JL
dc.contributor.authorNie, Qifan
dc.contributor.otherUniversity of Alabama Tuscaloosa
dc.date.accessioned2021-05-12T16:28:08Z
dc.date.available2021-05-12T16:28:08Z
dc.date.issued2020-12
dc.descriptionElectronic Thesis or Dissertationen_US
dc.description.abstractThe emergence of autonomous vehicles (AVs) presents people with new possibilities for daily travel. Since the first completion of Google’s self-driving car on-road test in 2009, a variety of automobile manufacturers, high-tech companies, and transportation network companies have joined the autonomous vehicle competition in order to have a leading role in the future transportation industry. Given their self-driving capabilities, AVs are expected to further promote the current shared mobility programs, including both car-sharing and ride-hailing services. Shared autonomous vehicles (SAVs) appear to be a promising future travel mode. This dissertation envisions an AV-supported shared mobility system in the Tuscaloosa, Alabama region. Tuscaloosa is a college town (UA) with a large group of young and high-educated individuals who could be among the first SAV user groups. This dissertation investigates the public perceptions towards shared mobility services powered by AVs. Twitter data were used to capture the perceptions among the general public and a student-centered survey was conducted among UA students to understand how this demographic perceives SAVs. Next, this dissertation exploits open-source social-economic data to construct high-resolution daily travel patterns to simulate a fleet of SAVs serving Tuscaloosa residents. An agent-based activity-based simulation was developed to envision the operations of an SAV system serving travelers in Tuscaloosa. This dissertation makes significant contributions in three aspects. First, the dissertation presents general impressions from social media alongside college students’ specific perceptions toward AV-supported shared mobility services, revealing public beliefs and concerns about SAV concepts and the characteristics of potential SAV users in future implementation. Second, this dissertation provides one of the first studies into development of data-intensive high-resolution travel behavior and mobility service simulations for small cities where data are often limited. A framework for preparing data for such simulations is presented in this dissertation. Third, the findings show the potential operational characteristics (e.g., demand) and consequences (e.g., empty vehicle miles) of implementing a college town SAV system. Decision-makers may utilize these findings to determine the feasibility of introducing SAVs in their cities, and operators or investors can gain insights regarding the operations of SAVs (e.g., fare rates, fleet size) in a region.en_US
dc.format.extent183 p.
dc.format.mediumelectronic
dc.format.mimetypeapplication/pdf
dc.identifier.otheru0015_0000001_0003684
dc.identifier.otherNIE_alatus_0004D_14383
dc.identifier.urihttp://ir.ua.edu/handle/123456789/7627
dc.languageEnglish
dc.language.isoen_US
dc.publisherUniversity of Alabama Libraries
dc.relation.hasversionborn digital
dc.relation.ispartofThe University of Alabama Electronic Theses and Dissertations
dc.relation.ispartofThe University of Alabama Libraries Digital Collections
dc.rightsAll rights reserved by the author unless otherwise indicated.en_US
dc.subjectCivil engineering
dc.subjectTransportation
dc.titlePreparing small urban areas for shared mobility with autonomous vehicles: a case study of college townen_US
dc.typethesis
dc.typetext
etdms.degree.departmentUniversity of Alabama. Department of Civil, Construction, and Environmental Engineering
etdms.degree.disciplineCivil, Construction & Environmental Engineering
etdms.degree.grantorThe University of Alabama
etdms.degree.leveldoctoral
etdms.degree.namePh.D.

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