LAUSR

Model-based traffic state estimation is used to reproduce traffic flow states from available observation data to assist traffic control and management. Owing to temporal and spatial observation limitations, numerous unknown traffic states and parameters exist in a nonlinear traffic flow model. These cannot be accurately estimated using filter methods with unavoidable parametric assumptions. In addition, the difficulty of estimation increases owing to an increased number of diverse traffic flow states and more unfavorable observation conditions at signalized intersections compared with those on the freeway. To overcome these problems, in this study, we developed switching state-space models to approximate the description of dynamic traffic flows at signalized intersections. By setting the traffic flow rate at the upstream and downstream boundaries of the road as observation data, we constructed a Bayesian learning framework in which all unknown variables in the models can be learned from the observed data. Finally, the utility of our method is demonstrated with the synthetic and the real NGSIM data. The result demonstrated that the proposed method could perform reasonably well in estimating the traffic flow dynamic process and states at signalized intersections, and could constitute an efficient scheme that avoids critical dependence on parametric calibrations and assumptions.