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This paper deals with the problem of predicting route flows (and hence, Origin-Destination (OD) pair and link flows) and updating these predictions when plate scanned information becomes available. To this end, a normal Bayesian network is built which is able to deal with the joint distribution of route and link flows and the flows associated with all possible combinations of scanned link flows and associated random errors. The Bayesian network provides the joint density of route flows conditioned on the observations, which allow us not only the independent or joint predictions of route flows, but also probability intervals or regions to be obtained. A procedure is also given to select the subset of links to be observed in an optimal way. An example of application illustrate the proposed methodology and shows its practical applicability and performance.

PREDICTING TRAFFIC FLOWIN ROAD NETWORKS - Using Bayesian Networks with Data from an Optimal Plate Scanning Device Location