Accurate depiction of existing traffic states is essential to devise effective real-time traffic management strategies using Intelligent Transportation Systems (ITS). Existing applications of Dynamic Traffic Assignment (DTA) methods are mainly based on either the prediction from macroscopic traffic flow models or measurements from the sensors and do not take advantage of traffic state estimation techniques, which produce estimate of the traffic states with less uncertainty than the prediction or measurement alone. On the other hand, research studies highlighting estimation of real-time traffic state are focused only on traffic state estimation and have not utilized the estimated traffic state for DTA applications. This research introduces a framework which integrates real-time traffic state estimate with applications of DTA to optimize network performance during uncertain traffic conditions through traveller information system. The estimate of real-time traffic states is obtained by combining the prediction of traffic density using Cell Transmission Model (CTM) and the measurements from the traffic sensors in Extended Kalman Filter (EKF) recursive algorithm. The estimated traffic state is used for predicting travel times on available routes in a traffic network and the predicted travel times are communicated to the commuters by a variable message sign (VMS). In numerical experiments, the proposed estimation and information framework is applied to optimize network performance during traffic incident on a two route network. The proposed framework significantly improved the network performance and commuters’ travel time when compared with no-information scenario during the incident. The application of the formulated methodology is extended to model day-to-day dynamics of traffic flow and route choice with time-varying traffic demand. The day-to-day network performance is improved by providing accurate and reliable traveller information. The implementation of the proposed framework through numerical experiments shows a significant improvement in daily travel times and stability in day-to-day performance of the network when compared with no-information scenario. The use of model based real-time traffic state estimation in DTA models allows modelling and estimating behaviour parameters in DTA models which improves the accuracy of the modelling process. In this research, a framework is proposed to model commuters’ level of trust in the information provided which defines the weight given to the information by commuters while they update their perception about expected travel time. A methodology is formulated to model and estimate logit parameter for perception variation among commuters for expected travel time based on measurements from traffic sensors and estimated traffic state. The application of the proposed framework to a test network shows that the model accurately estimated the value of logit parameter when started with a different initial value of the parameter.