Modeling individuals’ preferences towards different levels of vehicle autonomy: A random parameter rank-ordered logit model

Abstract

Vehicular automation is changing the conventional way of driving – e.g., the role of a driver is changing into an observer or a passenger due to the higher levels of automation. However, the success of this vehicle automation potentially leading to fully autonomous vehicles largely depends on the interest and comfort of consumers. Adoption of such disruptive technologies might have a retrospective aspect related to individuals’ experiences, such as exposure to technologies over their life course. This study adopts a life history-oriented approach to investigate individuals' preferences towards different levels of vehicle autonomy. The effects of historical experiences in different life domains such as technology usage in daily life and availability of vehicle technology, and the evolution of demographics such as historically living in an owned dwelling are explored. Data comes from a retrospective survey conducted in the Okanagan region of Canada. A random parameter rank-ordered logit model is developed to accommodate the relative ranked preferences of the alternatives and to capture unobserved heterogeneity. The model results suggest that individuals with historical exposure to vehicle technology have a higher likelihood of adopting higher levels of vehicle automation, whereas individuals having advanced technology only in the current vehicle reveal a lower interest towards higher vehicle autonomy. Individuals without a driver’s license show a higher interest in full automation, whereas they are less interested in no automation. The model confirms the existence of heterogeneity. For instance, pro-urban individuals prefer no automation, whereas pro-suburban individuals are inclined towards full automation. Both variables show significant heterogeneity with a large standard deviation. The findings of this study demonstrate the need to accommodate the effects of historical experiences while analyzing the preferences towards different levels of automation. Such inclusion of life history-oriented attributes within the travel demand forecasting models is expected to improve the demand and usage prediction accuracy of different levels of vehicle automation.