Traffic State Estimation Method for Arterial Street

For the effective operation and evaluation of these systems, the traffic state information is crucial and necessary. This paper presents a traffic state estimation method based on the Cell Transmission Model (CTM) and Kalman Filter Algorithm for urban Arterial Street. This paper partitioned link into cells according as CTM and designed a traffic state estimator based on Kalman Filter Theory. In each time interval, the estimator used the real-time data colleted by fixed sensors and the probes to update the traffic parameters in each cell, including number of vehicles, density and speed. Thus the traffic state of the whole link could be obtained. Finally, the performance of the proposed method was tested with simulated data. Test results show that the method can be applied to estimate the traffic state and the queue length.