Modeling of path-choice behavior based on the regret minimization criteria givena full travel information

Abstract

This article formulates the drivers' behaviors of path-choice when travel information for a set of paths is given to each driver after finishing his or her trip.While an underpinning theory of the behavioral model stems from a regret matching theory, the basic framework is adjusted to capture the characteristic of congested networks. The notable feature of the model is that each driver is treatedas an individual decision maker as in game theory. In addition, uncertainty thata driver faces is endogenously generated as the results of mutually dependent path-choice behavior. Drivers are modeled to achieve no-regret criteria and to learn uncertain environment with stochastic approximation.Dynamic of updating the propensity of path-choice is characterized by the ODE approach, and is shownto converge to an internally chain recurrent set. However, this is not the case for the congested network with monotone cost functions. If we admit monotone cost function, then the regret matching approach with stochastic approximation has very nice feature that allows a generic travel cost function such as asymmetric Jacobian, piece-wise linear and so on.