Drivers' adaptive expectations formation in nonstationary traffic environment

Abstract

In this paper, we examine the modeling process of traffic conditions that drivers learn such as travel time through longterm learning behavior under a nonstationary traffic environment. We found that rational expectations formation about the route travel time can be expressed by the adaptive expectations model when the travel time changes in accordance with a nonstationary process which consists of permanent shock and transient shock. We found that the adaptive parameter of the model converges to a fixed value corresponding to the route conditions. Furthermore, when the traffic environment follows stationary processes, the expectations of the traffic agent are congruent with the rational expectations. In other words, rational expectations formation can be regarded as a special case of the expectation formation model that is proposed in this research. The paper is concluded by illustrating some numerical examples.