Modeling and Control of Heterogeneous and Mixed-autonomy Traffic Flow

Automated or partially automated vehicles (AVs) are already present on our roadways. Raphael Stern is interested in the impacts AVs will have on traffic flow. This automation of vehicles may take the form of fully autonomous vehicles with no human intervention (SAE* Level 5) or, as is already the case in many modern vehicles, the form of driver assist features, such as adaptive cruise control or other SAE Level 1 and 2 features. Regardless of the extent of automation, the introduction of AVs has the potential to substantially alter traffic flow while also providing new opportunities to control the traffic flow. In this talk, Raphael Stern presents some of his recent research conducted to understand how AVs will influence traffic flow at the individual vehicle level and then to derive macroscopic flow properties that result from these changes in individual driving dynamics. Then, using the derived macroscopic traffic flow, he develops new control techniques that achieve the desired traffic state and reduce overall travel time. His work provides a roadmap for how vehicle automation might impact traffic dynamics, and how to design new traffic controllers that leverage these flow dynamics and reduce travel time for all drivers.

*SAE International is the world’s leading authority in the development of mobility standards. 

SPEAKER
Raphael Stern is an Assistant Professor of Transportation Engineering in the Department of Civil, Environmental, and Geo- Engineering at the University of Minnesota. His research interests include vehicle automation and transportation cyber-physical systems, specifically, the impact of autonomous (or semi-autonomous) vehicles on the overall traffic flow and dynamics at low market penetration rates. Stern is also interested in data science and machine learning approaches to transportation data analysis.