Modelling individuals’ longer-term preferences towards autonomous vehicles and their effects on vehicle ownership

Autonomous vehicle (AV) promises to change the transportation landscape such as reducing traffic congestion and emissions. The success of this emerging technology largely depends on how individuals will adopt it. This thesis investigates individuals’ longer-term preferences for AVs, and their effects on vehicle ownership, specifically focusing on the preferences towards different levels of vehicle automation, AV ownership (AVO) and AV sharing (SAV), and vehicle transaction decisions in an AV future. A life history-oriented approach is adopted to examine the effects of historical experiences and changes over the life course, such as historical exposure to technology, the evolution of household characteristics, vehicle ownership history, historical measures of accessibility and built-environment on AV adoption. In addition, variables representing attitudinal factors, travel attributes, and socio-demographics are accommodated in the study. Data for the thesis comes from the retrospective survey conducted for the Okanagan region of British Columbia. In the case of preferences for different levels of vehicle automation, a random parameter rank-ordered logit model is developed to accommodate rank-ordered preferences for vehicular automation levels and capture unobserved heterogeneity. For preferences towards AVO and SAV, a joint bivariate ordered probit model is developed to address the error correlation between decisions. The study confirms that a significant error correlation exists, which indicates that the unobserved factors jointly affect the choice alternatives. A latent class random parameter logit modelling technique is utilized to capture heterogeneity while modelling vehicle transaction decisions in the AV future. The model results confirm the existence of historical deposition effects on individuals’ preferences towards AVs. For example, individuals with historical exposure to vehicle technology, such as availability of lane assist, parking assist, and autonomous emergency stop, have a higher likelihood of adopting higher levels of vehicle automation. Besides, they are more likely to add AV to the current vehicle fleet. Similarly, the AVO and SAV model results suggest that individuals with a higher number of smartphone ownership over the life course are more likely to adopt both. The model results also confirm the existence of heterogeneity. Overall, the findings of the study provide important behavioural insights and have significant policy implications that might be used for targeted marketing to promote AVs.Applied Science, Faculty ofEngineering, School of (Okanagan)Graduat