Urban traffic state estimation for signal control usingmixed data sources and the extended Kalman filter

摘要

This paper describes a methodology for fusing data from multiple sensors, including wireless devices and inductive loops, to make an estimation of the instantaneous state of an urban traffic network. An extended Kalman filter is employed along with a state evolution model to make estimates of the state in a discretized network. The instantaneous state is an estimate of the current distribution of vehicles in the network and their instantaneous speeds. Microsimulation tests were used to evaluate the performance of the state estimation on a small urban networks. These results indicate low error between the estimated state and the known ground truth.