Bayesian network model for traffic flow estimation using prior link flows

In order to estimate traffic flow, a Bayesian network (BN) model using prior link flows is proposed. This model sets link flows as parents of the origin-destination (OD) flows. Under normal distribution assumptions, the model considers the level of total traffic flow, the variability of link flows and the violation of the conservation law. Using prior link flows, the prior distribution of all the variables is determined. By updating some observed link flows, the posterior distribution is given. The variances of the posterior distribution normally decrease with the progressive update of the link flows. Based on the posterior distribution, point estimations and the corresponding probability intervals are provided. To remove inconsistencies in OD matrices estimation and traffic assignment, a combined BN and stochastic user equilibrium model is proposed, in which the equilibrium solution is obtained through iterations. Results of the numerical example demonstrate the efficiency of the proposed BN model and the combined method.