A traffic assignment model is presented that simulates the process of users' choice in a day-to-day dynamic framework and also the traffic dynamics "within" each single simulated day. It can therefore be considered a doubly dynamic model, being day-to-day dynamic to represent user path choice and within-day dynamic to represent user movement on the network. The model has fixed departure times; origin-destination demand flows are assumed known at each interval within the simulated period. Path choice behavior is modeled considering explicitly learning or information updating processes for experienced path costs. The users' choice adjustment process takes into account the inertia to day-to-day path changes. Path choices are simulated by applying a C-logit random utility model, that allows the effects of links shared between similar paths to be simulated, keeping an analytical expression of choice probability. The Network Flow Propagation model adopted in the dynamic traffic assignment proposed in this paper, is based on a mesoscopic simulation model and explicit path enumeration. The model is intended mainly for planning applications; computation times, at the moment, are not compatible with real time application.