Adoption Behavior of Privately Owned Autonomous Vehicles

The research detailed in this dissertation provides a comprehensive analysis of market penetration of privately owned autonomous vehicles (AVs) and elaborates on several dimensions of people’s adoption behavior. Research in the area of autonomous mobility is advancing at a fast pace, heavily centered around estimating the potential impacts of the technology on travel behavior, network operation, and the environment. While highly informative, most of these studies inevitably suffer from the absence of a sound estimation of AVs’ market penetration and establish their assessments based on the speculations regarding people’s adoption behavior. Current research is conducted to address such a gap by focusing on multiple dimensions of people’s AV adoption behavior such as sensitivity of their adoption decision to the vehicle attributes, their willingness to pay, their preferred AV adoption timing, and the potential variance in adoption behavior of people from different generations. The research is designed to thoroughly investigate how individual- and household-level demographics, travel preferences, social factors, and built-environment characteristics affect each dimension of people’s adoption behavior. To collect the required data for the study, a comprehensive web-based stated preference (SP) survey has been designed and conducted in the Chicago metropolitan area. All results presented in this dissertation are based on the data derived from the implemented survey. Various behavioral theories from the well-known utility maximization theory to the innovation diffusion theory have been adopted to improve the behavioral realism of the models. Further, several advanced methodological approaches have been employed to address the limitations of the traditional models. Estimating a heterogeneity-in-means random parameters best-worst model as an advanced alternative of the select-one-choice models, developing a random parameters random thresholds hierarchical ordered probit model for estimating people’s willingness-to-pay for AVs, and developing a joint model of vehicle fuel choice and automation choice to capture the potential correlation between the two decisions and account for the shared unobserved factors that might affect them are amongst the methodological contributions of the current research.