A Day-to-day route choice model based on drivers’ past experience

The objective of this study is to develop a non UE (User Equilibrium)-based traffic assignment model which is able to be applied to a real size network. Over the last few decades, the UE principle has been used as a standard route choice principle. However, it also has received considerable criticism since it is difficult to fully describe the route choice behavior of real drivers. The real drivers select a route based on their cumulated experiences, and their perceived travel times are also not deterministic values, but probabilistic values which reflect the various uncertainties existing in the network. If a driver frequently uses a specific route, then the driver has better information on that route than other routes. As a result, a marginal superiority of other paths might not make the driver quit using the frequently selected path. Accordingly, a network traffic pattern is not so sensitive to the change of travel environment and drivers tend to select routes stably and habitually. In addition, the cumulated information in drivers’ minds and the travel experiences are also important to develop traffic operation and management schemes. However, a UE-based traffic assignment model cannot take into account the previous travel experiences and network uncertainties. Therefore, the route choice in the UE-based model might be over-sensitive to the change of travel environment in the network or occasionally forecasted incorrectly because of the absence of consideration for previous travel experiences and perception limit. In order to overcome these problems, the authors developed an agent-based traffic assignment model while making use of “reference point” theory in the field of psychology and “bounded rationality” used in decision making theory. In the developed model, travel time is modeled not as a deterministic value, but as the concept of probability density function in order to reflect the network uncertainties with which the real drivers face in the real world. In addition, the density function is linearly simplified in order to improve applicability to the real-size transportation network problems.