Bayesian estimation of traffic lane state

摘要

Modelling of large transportation systems requires a reliable description of its elements that can be easily adapted to the specific situation. This paper offers mixture model as a flexible candidate for modelling of such clement. The mixture model describes particular and possibly very different states of a specific system by its individual components. A hierarchical model built on such elements can describe complexes of big city communications as well as railway or highway networks. Bayesian paradigm is adopted for estimation of parameters and the actual component label of the mixture model as it serves well for the subsequent decision making. As a straightforward application of Bayesian method to mixture models leads to infeasible computations, an approximation is applied. For normal stochastic variations, the resulting estimation algorithm reduces to a simple recursive weighted least squares. The elementary modelling is demonstrated on a model of traffic flow state in a single point of a roadway. The examples for simulated as well as real data show excellent properties of the suggested model. They represent much wider set of extensive tests made.