This paper presents a freeway O-D (Origin-Desination) demand prediction algorithm using an adaptive Kalman Filtering technique where the effect of information on users' route diversion behavior has been explicitly modeled using a dynamic, aggregate, route diversion model. The inherent dynamic nature of the traffic flow characteristics is captured using a time-variant kalman Filtering modeling framework. Changes in drivers' 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 in real-time using a Bayesian updating approach. The impact of route diversion on freeway O-D demands has been integrated in the estimation framework. The proposed methodology is evaluated using data obtained form a microscopic traffic simulator, INTEGRATION. Experimental results on a freeway incident management system establish that significant improvement in predicction could be achieved by explicitly accounting for route diversion behavior.
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