Predicting the ownership, use, and environmental impacts of new vehicle technologies with a focus on the relationship between travel behavior and the built environment

dc.contributor.advisorKockelman, Kara
dc.creatorNodjomian, Adam Taylan
dc.creator.orcid0000-0002-3046-5402
dc.date.accessioned2019-05-07T16:48:41Z
dc.date.available2019-05-07T16:48:41Z
dc.date.created2018-12
dc.date.issued2018-12
dc.date.submittedDecember 2018
dc.date.updated2019-05-07T16:48:41Z
dc.description.abstractThe field of transportation is on the cusp of major change. Innovations in how vehicles operate and are powered have the potential to elicit changes not seen since the introduction of the interstate highway system more than half a century ago. Predicting the impacts of new vehicle technologies has interested researchers and practitioners across disciplines and continents. This thesis makes a handful of such predictions. It is divided into three parts. In the first part, the results of two large-scale preference surveys and data from the U.S. Environmental Protection Agency’s (EPA) Smart Location Database (EPA, 2014) are used to estimate how land use characteristics impact Americans’ perceptions of, interest in, and willingness to pay for new vehicle technologies, while controlling for demographic attributes. The surveys were conducted by Quarles and Kockelman (2018) and Gurumurthy and Kockelman (2018) in 2017 and together represented over 4,000 U.S. households. Statistical models like the ordered probit and multinomial logit are used to estimate the impacts of demographics and land use characteristics on vehicle-related behavior. Various land use variables arise as significant depending on the question being asked of the respondents. For example, poor job accessibility via automobile is associated with higher levels of interest in automated vehicles (AVs), higher anticipated use of AV technology, a willingness-to-pay (WTP) for self-driving capability, and a greater reliance on AVs for some long-distance travel. No land use variable arises as significantly more predictive than the others at this national-level scale of analysis. The results emphasize the fact that land use policy must be considered at the local level, and that there is no “one size fits all” solution for managing future transportation behavior with land use action. The second part of this thesis evaluates the connection between land use and current travel behavior. Census tract-level measures of population and employment density (provided once again by the EPA’s Smart Location Database [EPA, 2014]) are evaluated across the nation to investigate the connection between the development conditions one experiences and his or her travel behavior. Travel data comes from the 2009 National Household Travel Survey (NHTS). The results highlight a stronger connection between population density and vehicle-miles traveled (VMT) and vehicle ownership, than with employment density. For both VMT and vehicle ownership, an improvement of only two to one can be expected by changing population density conditions in a census tract. In other words increasing population from the lowest density conditions to the highest results in a decline of VMT per capita per day from 20 miles to 10 miles. Similarly, vehicle ownership per capita generally ranges from 0.4 to 0.8. Notably, these improvements are not realized until the highest decile of population density (18 people per acre), thus indicating that simply building homes in rural or low-density suburban regions will likely have a negligible impact on transportation demand. Employment density was found to be less indicative of travel behavior. The third and final piece of the thesis predicts how an evolving light-duty vehicle (LDV) fleet will impact the amount of energy consumed by Americans and the emissions they create. Here, the results of a fleet evolution simulation, developed by Quarles et al. (2019), are used to project what a vehicle fleet with more electric (and fewer gasoline-powered) vehicles will mean for energy consumption and emissions on a per capita basis. Projections are based on historic fuel efficiency data and emission production rates from the Bureau of Transportation Statistics (BTS) and EPA (BTS, 2018b; BTS, 2018c; EPA, 2018a). Conclusions from these findings highlight the need for more efficient vehicles, better emissions control technologies on existing vehicle models and power plants, and a decreased reliance on highly-polluting energy sources for power generation. Policies aimed at achieving these objectives will help ensure that Americans’ future vehicle behavior and ownership will not create an undue burden on themselves or the environment in which they live. Although the analyses discussed in this thesis cover diverse topics such as human behavior, urban planning, and air quality, they establish the need for a proactive approach to cutting-edge vehicle technologies. If left to develop without any oversight or action, transportation network congestion will worsen, development will continue to sprawl, and the environment and public health will suffer. Policies aimed at limiting “empty” driving with AVs, increasing population density, and curbing vehicle and power plant emissions can help ensure the benefits of vehicle technology innovation are not realized at the expense of other considerations.
dc.description.departmentCivil, Architectural, and Environmental Engineering
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/2152/74510
dc.identifier.urihttp://dx.doi.org/10.26153/tsw/1630
dc.language.isoen
dc.subjectAutonomous vehicles
dc.subjectShared
dc.subjectTravel behavior survey
dc.subjectModels
dc.subjectWillingness to pay
dc.subjectMode choice
dc.subjectPopulation density
dc.subjectEmployment density
dc.subjectFleet evolution
dc.subjectEnergy usage
dc.subjectEmissions
dc.titlePredicting the ownership, use, and environmental impacts of new vehicle technologies with a focus on the relationship between travel behavior and the built environment
dc.typeThesis
dc.type.materialtext
thesis.degree.departmentCivil, Architectural, and Environmental Engineering
thesis.degree.disciplineCivil Engineering
thesis.degree.grantorThe University of Texas at Austin
thesis.degree.levelMasters
thesis.degree.nameMaster of Science in Engineering
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