New Multiclass Traffic Equilibrium Assignment Model with Similarity Measure
A novel user equilibrium traffic assignment model in view of the drivers' different familiar levels with the network  is presented in this paper. In the model, drivers are divided into classes according as their knowledge about the network traffic condition and travel-decision characteristics are the same within each class, but differ among classes. Some new concepts, such as OD perceived cost and familiar level with the traffic condition, are introduced based on $\alpha-$cut of fuzzy set. The equilibrium principle is defined based on the similarity measure and can be regarded as a generalization of Wardrop's user equilibrium (UE). A proposed numerical example show that the new model obtains more realistic results than the other two models, UE and SUE.