Project
Suburban Areas Most Affected by Automated Driving

Self-driving cars have the potential to significantly shape the future of urbanization in the developed world. This project explored the effects of automated driving on urbanized areas — a topic of mutual interest to the mobility industry, and to urban designers and planners. Popular tech media variously speculates on the potential concentration and expansion effects of automated driving. As a new mobility form, projections about automated driving remain hypothetical. This project attempted to define how automated driving options might alter urbanization patterns, as they penetrate the market, through the exploration of various scenarios.

Theoretically, a more dynamic set of mobility options (on-demand, self-driving, etc.), along with the consolidation of parking and fueling infrastructures, could promote denser urban clustering. Alternatively, an easier and more convenient form of transportation may drive demand for typically cheaper housing further from current urban zones. To evaluate these potential changes, the project was split into two phases. The first phase of the project assessed how the costs and benefits of automated driving technologies might be valued for the personal transportation sector. What are the cost estimates for these technologies? How will those costs decline over time? How might these costs be balanced by the potential time savings of automated travel? What are the potential interactions of automated driving with road capacity and induced travel demand? Collectively, addressing these questions through literature review and through a multi-disciplinary lens helped explore the potential economic value of traveling in self-driving cars, and thus how they might alter urbanization.

The second phase of the project focused more on the potential for automated driving, based on the derived costs and time gains, to alter the direction of urbanization. This included a broad range of urban areas, from dense districts to remote exurb developments, and the road system that connects them. Automated driving may interact with all these areas in potentially divergent ways. To study these interactions, the project combined recent research on driver behavior from collaborating partners at the Toyota Research Institute of North America (TRINA) with existing research on urbanization trends to help predict the potential effects of automated driving.

Laberteaux, K. P., Hamza, K., Berger, A. and Brown, C. L. (2017) Method for Gauging Usage Opportunities for Partially Automated Vehicles with Application to Public Travel Survey Data Sets. Transportation Research Record: Journal of the Transportation Research Board. 2625: 43-50

Spring 2015