Description:

Abstract

Car following (CF) models are fundamental to describing traffic dynamics. However, the CF behavior of human drivers is highly stochastic and nonlinear. As a result, identifying the “best” CF model has been challenging and controversial despite decades of research. Introduction of automated vehicles has further complicated this matter as their CF controllers remain proprietary. This talk will discuss a stochastic learning approach to integrate multiple CF models, rather than relying on a single model. The framework is based on approximate Bayesian computation that probabilistically concatenates a pool of CF models based on their relative likelihood of describing observed behavior. The approach, while data-driven, retains physical tractability and interpretability. 
 
 
Bio 

Soyoung (Sue) Ahn is a Professor in the Department of Civil and Environmental Engineering at the University of Wisconsin-Madison (UW-Madison). Her research interest lies in analysis and modeling of human-driven and connected automated vehicle flow. Her recent research involves 1) understanding human-automated vehicle interactions, 2) linking those interactions to traffic-level phenomena, and 3) applying this knowledge to develop automated vehicle and traffic control strategies. Her group has received several best paper awards, most recently D. Grant Mickle Award, Best Paper on Operation and Maintenance of Transportation Facilities, given by Transportation Research Board. Ahn is a chair of the Operations Section for the Transportation Research Board (TRB) and an elected member of the International Advisory Committee for the International Symposium on Traffic and Transportation Theory.