This paper investigates the day-to-day dynamics in an urban traffic network induced by joint route and departure time switching dynamics in commuter decisions under real-time information. The role of switching inertia, the user's sensitivity to experienced and lagged system attributes, and travel demand management measures on day-to-day evolution are analyzed. A simulation-based day-to-day network analysis framework is developed by incorporating empirically calibrated user behavior models under information. This model can be used to analyze network reliability and stability measures. Because it can model users' responses to information and experience, this framework permits relaxing the assumption that user behavior remains unchanged. Therefore, it can be used to model the transient effects due to various types of system perturbations and shocks. Computational experiments are used to investigate the effect of the aforementioned factors. The findings suggest that trip time variability and lateness propensity increase under high congestion. Under joint switching dynamics, a significant deviation from equilibrium is observed even after nearly 2 months. Departure time switching behavior appears to exert a greater influence than route switching on day-to-day dynamics, and transportation control measures can improve trip time and network reliability. These results have important implications for congestion mitigation strategies, network reliability assessment, and evaluation of intelligent transportation system technologies.