TRAFFIC NETWORK STATE ESTIMATION USING EXTENDED KALMAN FILTERING AND DSMART

For effective (network) traffic control, such as route guidance, one of the key requirements is to derive an estimate of the current state in the traffic network. A principle method of doing so, is to combine a traffic network model with available measurement data by means of an Extended Kalman filter (EKF). One of the virtues of using an EKF is that aside from an estimate of the mean traffic state on each link also an estimate of the state estimation error covariance matrix is obtained, which reflects the uncertainty in the state estimates. This paper describes how this can be done analytically in the case of a first order traffic flow model and discusses some preliminary results.