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dc.contributor.advisorKockelman, Karaen
dc.creatorBansal, Prateeken
dc.date.accessioned2015-10-20T15:29:07Zen
dc.date.available2015-10-20T15:29:07Zen
dc.date.issued2015-08en
dc.date.submittedAugust 2015en
dc.identifierdoi:10.15781/T2JS5Ven
dc.identifier.urihttp://hdl.handle.net/2152/31797en
dc.descriptiontexten
dc.description.abstractThis thesis is divided into four parts. The first part investigates the impact of built-environment and demographic attributes on adoption rates of hybrid electric vehicles and more fuel efficient vehicles. To allow for spatial autocorrelation (across census tracts) in unobserved components of tract-level vehicle counts, as well as cross-response correlation (both spatial and aspatial), vehicle counts by vehicle type and fuel economy levels were estimated using a bivariate and trivariate Poisson-lognormal conditional autoregressive model. Fuel-efficient-vehicle ownership rates were found to rise with household income, resident’s education levels, and the share of male residents, and fall in the presence of larger household sizes and higher jobs densities. In the second part, a fleet evolution framework is proposed to simulate Americans' long-term (year 2015 to 2045) adoption of connected and autonomous vehicle (CAV) technologies under eight-different scenarios based on: 5% and 10% annual drop in technology-prices; 0%, 5%, and 10% annual increment in Americans' willingness to pay (WTP); and NHTSA's regulations. A survey was designed and disseminated to obtain 2,167 Americans' preferences; and those data were used in simulation framework. The survey results indicate that Americans' average WTP (of the respondents with a non-zero WTP) to add connectivity and Level 3 and Level 4 automation are $110, $5,551, and $14,589, respectively. The simulation results suggest that 24.8% (at 5% drop in technology-prices and constant WTP) to 87.2% (at 10% drop in technology-prices and 10% WTP rise) of the Americans' privately owned vehicle-fleet will be fully-automated by 2045. The parts three and four summarize findings of two separate surveys, polling 1,088 Texans and 347 Austinites, respectively, to understand their opinions on CAV technologies and strategies. Ordered probit, interval regression, and other models are estimated to understand the impact of demographics, built-environment, and other attributes on Austinites' and Texans' WTP to add CAV technologies to their vehicles, as well as the adoption rates of shared AVs (SAVs) under different pricing scenarios, AV adoption timings' dependence on friends' adoption rates, and home-location decisions after AVs and SAVs become common modes of transport. The Texas study's results indicate that those who support speed regulation strategies, and have higher household income, are estimated to pay more for all CAV technologies, but older and more experienced licensed drivers are expected to place lower value on these technologies. The Austin study's results indicate that higher-income technology-savvy males, living in urban areas and those who have experienced more crashes, have higher WTP for the new technologies. Moreover, Texans and Austinites share a common perception and expect fewer crashes to be the primary benefit of AVs, with equipment failure being their top concern.en
dc.format.mimetypeapplication/pdfen
dc.language.isoenen
dc.subjectConnected and autonomous vehiclesen
dc.subjectHybrid electric vehicle ownershipen
dc.subjectSimulation-based fleet evolution frameworken
dc.subjectShared autonomous vehiclesen
dc.subjectWillingness to payen
dc.subjectOrdered probit modelsen
dc.titleSpatial modeling of electric vehicle ownership across Texas and a simulation-based framework to predict Americans' adoption of autonomous vehicle technologiesen
dc.typeThesisen
dc.date.updated2015-10-20T15:29:07Zen
dc.contributor.committeeMemberBoyles, Stephen Daviden
dc.description.departmentCivil, Architectural, and Environmental Engineeringen
thesis.degree.departmentCivil, Architectural, and Environmental Engineeringen
thesis.degree.disciplineCivil Engineeringen
thesis.degree.grantorThe University of Texas at Austinen
thesis.degree.levelMastersen
thesis.degree.nameMaster of Science in Engineeringen


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