Using Discrete Choice Experiments to assess early Consumer Response to Vehicle Automation

Presented by Ricardo Daziano, Associate Professor of Civil and Environmental Engineering, Cornell University

Autonomous vehicles use sensing and communication technologies to navigate safely and efficiently with little or no input from the driver. These driverless technologies will create an unprecedented revolution in how people move, and policymakers will need appropriate tools to plan for and analyze the large impacts of novel navigation systems. Using data from two discrete choice experiments, in this talk estimates of the willingness to pay for automation will be discussed. We have first found that the average household is willing to pay a significant amount for automation: about $3,500 for partial automation and $4,900 for full automation. Second, we estimate substantial heterogeneity in preferences for automation, where a significant share of the sample is willing to pay above $10,000 for full automation technology while many are not willing to pay any positive amount for the technology. The estimates also suggest that the demand for automation is split approximately evenly into high, modest and no demand, highlighting the importance of modeling flexible preferences for emerging vehicle technology. Preliminary results from a second case study show that users of shared services actually would like to be compensated for riding an automated vehicle.  


Ricardo Daziano, PhD in economics (Université Laval, 2011), is an associate professor of Civil and Environmental Engineering at Cornell University. He is also the associate director of the Engineering Management Program, a core faculty member of Systems Engineering, a member of the Regional Science graduate field, a fellow of the Atkinson Center for a Sustainable Future at Cornell, and a National Science Foundation CAREER Awardee (2013).. Daziano's research focuses on econometrics of consumer and travel behavior applied to technological innovation in transportation, the built environment, and energy. Conventional methods in discrete choice modeling treat forecasts as deterministic, but Daziano's research aims to overcome this limitation by deriving robust, computationally efficient statistical inference methods for stochastic policy-oriented analysis and non-market valuation. His goal is to better understand the interplay of consumer behavior and engineering, investment, and policy choices for energy-efficient technologies and for better, more resilient cities. Daziano's specific empirical research interests include the analysis of new urban transportation business models (mobility on demand), the study of preferences toward new vehicle technology (automation), modeling the adoption of sustainable travel behavior, estimating willingness-to-pay for renewable energy, and forecasting consumers' response to environmentally-friendly energy sources.

For further information about the ITS Seminar Series please see: