Highway Traffic State Estimation With Mixed Connected and Conventional Vehicles

Abstract

We present a macroscopic model-based approach for the estimation of the total density and flow of vehicles, for the case of “mixed” traffic, i.e., traffic comprising both ordinary and connected vehicles, utilizing only average speed measurements reported by connected vehicles and a minimum number (sufficient to guarantee observability) of spot-sensor-based total flow measurements. The approach is based on the realistic and validated assumption that the average speed of conventional vehicles is roughly equal to the average speed of connected vehicles, and consequently, it can be obtained at the (local or central) traffic monitoring and control unit from connected vehicles' reports. Thus, complete traffic state estimation (for arbitrarily selected segments in the network) may be achieved by estimating the total density of vehicles. Recasting the dynamics of the total density of vehicles, which are described by the well-known conservation law equation, as a linear parameter-varying system, we employ a Kalman filter for the estimation of the total density. We demonstrate the fact that the developed approach allows for a variety of different measurement configurations. We also present an alternative estimation methodology in which traffic state estimation is achieved by estimating the percentage of connected vehicles with respect to the total number of vehicles. The alternative development relies on the alternative requirement that the density and flow of connected vehicles are known to the traffic monitoring and control unit on the basis of their regularly reported positions. We validate the performance of the developed estimation schemes through simulations using a well-known second-order traffic flow model as ground truth for the traffic state.