A Day-to-Day Traffic Dynamic Model with Asymmetric Inertia and Preferences : A Laboratory Experimental Study

Day-to-day traffic (DTD) dynamic systems have attracted much attention recently as a powerful tool to describe the evolution of network flows over time. A number of DTD models adopting various behavioral principles have been proposed, but for most of them, either their assumptions are introduced without empirical evidence in transportation or their parameters are not yet calibrated by field or experiment data. This study is based on from a set of laboratory experiments designed to discover human subjects' route-choice patterns in congestible parallel-route networks. First, we find a few behavioral patterns inconsistent with classic theoretical assumptions, including the probabilistic choice, choice inertial and asymmetric preferences regarding the capacity of routes. Accordingly, a deterministic DTD model is proposed inspired by the behavioral patterns and proved to have a unique equilibrium state and a stable evolution process. The results of model calibration indicate that the model can fully reproduce the experimental observations, including equilibrium network flows and the switching rates of travelers. This work probably contributes to better understanding of actual commuters' route-choice behavior and the day-to-day evolution of network flows