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