An Unscented Kalman Filter for Freeway Traffic Estimation

This paper addresses the problem of freeway traffic flow estimation. The freeway is considered as a network of components representing different freeway stretches called segments. The evolution of the traffic in a segment is modelled as a dynamic stochastic system, influenced by states of neighbour segments. Measurements are received only at boundaries between some segments and averaged within regular time intervals. An Unscented Kalman filter is developed and its performance is compared with a particle filter both for synthetic data and for real traffic data. The intended application is to supply traffic control systems with the estimated traffic state.