An enhanced Kalman Filtering algorithm for the dynamic estimation and prediction of freeway origin-and-destination (O-D) matrices is presented. The effects of traffic congestion and traffic diversion information on the O-D distribution pattern are explicitly captured through a behavioral model of route switching. Moreover, in view of the time-varying nature of traffic variables, the proposed algorithm updates the model parameters by using on-line traffic measurements. Preliminary simulation results demonstrate the importance of using time-dependent model parameters and accounting for the effect of traffic information in the estimation and prediction of dynamic freeway O-D demands.
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