Connectivity and automation are two technologies that promise to make self-driving cars possible, transforming today's urban transportation system and revolutionizing mobility. Although there have been considerable research developments mainly focused on the technological impact of self-driving cars, e.g., managing congestion, reducing emissions and energy consumption, and improving safety, it is apparent that the social consequences and implications of self-driving cars have not been fully investigated and understood. A question we ask is, "How may human travelers, passengers, and drivers interact with self-driving cars, and what may be the effects on tendency-to-travel and travel demand?"
This research proposal aims to remedy our lack of understanding between human interaction and connectivity and automation in emerging mobility systems. Specifically, the vision of this proposal is to press forward with an implementable solution framework that will accelerate the safe deployment of self-driving cars and smooth the transition to fully automated transportation systems while ensuring that any emerging rebound effects are well-understood and alleviated, even prevented. The tools that we plan to develop will provide an integrative framework through which the broader social impacts of connectivity and automation will be identified, evaluated, and controlled. We aspire to develop mathematical tools that prevent the emergence of social inefficiencies, i.e., rebound effects, in mobility systems characterized by a technological and social dimension.