The Adoption of Automated Vehicles: Accounting for Attitudes and Heterogeneity

The interest in Automated Vehicles (AVs) among transportation researchers stems from the potential benefits in improving traffic congestion, reducing roadway accidents, and providing a viable mobility option that lessens transportation barriers. While AVs are anticipated to provide substantial benefits to society and travelers, the actual impacts need to be estimated before the emergence of this technology so policymakers can better plan the transportation system. A major challenge in measuring this impact originates from the lack of a clear understanding about people’s inclination to adopt this mobility option and their perception of this technology. This dissertation presented a comprehensive analysis of travelers’ decisions to adopt and pay for AVs while considering their attitudes toward this technology in terms of safety, privacy, reliability, and efficiency and also their heterogeneous preferences. Multiple analytical frameworks were proposed and applied to the data obtained from a nationally representative Stated Preference (SP) survey. The structural equation modeling approach showed a robust performance, and all indices satisfied the cut-off criteria such as Root Mean Square Error of Approximation (RMSEA=0.0240.95), Comparative Fit Index (CFI=0.973\u3e0.95), and Standardized Root Mean Square Residual (SRMR=0.03 In addition, this dissertation investigated the persistence of inclination toward AV technology among Millennials and Generation X to understand the degree to which their propensity to adopt AVs may be sustained in the future. The younger generation revealed a significantly higher inclination toward shared mobility, transit, driving assistance, and full automation features, while the older generation exhibited a higher tendency toward private vehicles. The findings of the Blinder-Oaxaca decomposition showed that the actual taste differences between the generations accounted for a major portion of the disparities (more than 70%), implying that attitudinal disparities are likely to persist and remain at significant magnitudes because they originated from the different perspectives between the generational cohorts. This study laid the foundation for understanding user acceptance and adoption of driverless cars, which is essential to estimating the likelihood and magnitude of travel behavior shifts in the era of smart cities