Freeway Network Density Estimation Based On Dynamic Graph Hybrid Automata Model By Using Kalman Filter

Freeway traffic state estimation is the major assurance of traffic management and control. This paper addresses a traffic density estimator for Beijing third ring freeway. First, we apply a new modeling method called Dynamic Graph Hybrid Automata (DGHA) to express the traffic dynamics and the evolution law of the freeway network by piecewise-linear. On this basis, it is feasible to use a open source software named Open-Modelica to establish Beijing third ring freeway network model. However, the presence of noise in detected data from sensors increases the difficulty of density estimation, so a Kalman filter is employed to obtain the high accuracy density of each network section based on the freeway network model. Finally the proposed estimate solution is validated through simulation results, illustrating the ability and potential of the density estimation.