The effectiveness of user-oriented traffic routing applications to mitigate traffic congestion in Intelligent Transportation Systems depends on their degree of adoption, which usually evolves depending on subjective and exogenous factors. This paper proposes a user experience and social dynamics model to analyze and evaluate traffic routing methods, based on fuzzy rules and discrete choice theory. The model has been applied to the optimal Traffic-Weighted Multi-Maps (TWM) routing method to evaluate the adoption dynamics and analyze convergence towards the system optimum. Route unfairness and resistance to change are also considered in the model. Experimental results are obtained simulating the evolution of the drivers' population behavior. Simulation is carried over synthetic and real networks, using optimized TWM maps. The experimental results show how the TWM system evolves to a stationary System Optimum, improving overall traffic congestion and showing how User Equilibrium variability is bounded as it depends on user routing choices influenced by behavioral patterns
