The primary focus of this research is to develop an approach to capture the eect of travel time information on travelersÕ route switching behavior in real-time, based on on-line trac surveillance data. It also presents a freeway Origin±Destination demand prediction algorithm using an adaptive Kalman Filtering technique, where the eect of travel time information on usersÕ route diversion behavior has been explicitly modeled using a dynamic, aggregate, route diversion model. The inherent dynamic nature of the trac ¯ow characteristics is captured using a Kalman Filter modeling framework. Changes in driversÕ perceptions, as well as other randomness in the route diversion behavior, have been modeled using an adaptive, aggregate, dynamic linear model where the model parameters are updated on-line using a Bayesian updating approach. The impact of route diversion on freeway Origin±Destination demands has been integrated in the estimation framework. The proposed methodology is evaluated using data obtained from a microscopic trac simulator, INTEGRATION. Experimental results on a freeway corridor in northwest Indiana establish that signi®cant improvement in Origin±Destination demand prediction can be achieved by explicitly accounting for route diversion behavior.