Modeling the effects of traveler information on freeway origin-destinationdemand prediction

The primary focus of this research is to develop an approach to capture the effect of travel time information on travelers' route switching behavior i n real-time, based on on-line traffic surveillance data. It also presents a freeway Origin-Destination demand prediction algorithm using an adaptive K alman Filtering technique, where the effect of travel time information on u sers' route diversion behavior has been explicitly modeled using a dynamic, aggregate, route diversion model. The inherent dynamic nature of the traff ic flow characteristics is captured using a Kalman Filter modeling framewor k. Changes in drivers' perceptions, as well as other randomness in the rout e diversion behavior, have been modeled using an adaptive, aggregate, dynam ic linear model where the model parameters are updated on-line using a Baye sian updating approach. The impact of route diversion on freeway Origin-Des tination demands has been integrated in the estimation framework. The propo sed methodology is evaluated using data obtained from a microscopic traffic simulator, INTEGRATION. Experimental results on a freeway corridor in nort hwest Indiana establish that significant improvement in Origin-Destination demand prediction can be achieved by explicitly accounting for route divers ion behavior. (C) 2001 Elsevier Science Ltd. All rights reserved.