This paper presents a method to model a driver's en route learning process and changes in route choice at each decision node. A model based on Bayesian networks is proposed to describe the en route updating of the driver's knowledge of the traffic state. A random utility–based model is developed to predict en route choices. A case study based on probe data is carried out to illustrate the development of the model and analyze the dynamic route choice problem. The results show that the model in which a driver's choice of making decisions en route is taken into account has a better goodness of fit. The probability of making a choice en route is related to the distance from the origin and the spatial scale of the intersection at the decision node.
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