Traffic State Estimation for Connected Vehicles using the Second-Order Aw-Rascle-Zhang Traffic Model

This paper addresses the problem of traffic state estimation (TSE) in the presence of heterogeneous sensors which include both fixed and moving sensors. Traditional fixed sensors are expensive and cannot be installed throughout the highway. Moving sensors such as Connected Vehicles (CVs) offer a relatively cheap alternative to measure traffic states across the network. Moving forward it is thus important to develop such models that effectively use the data from CVs. One such model is the nonlinear second-order Aw-Rascle-Zhang (ARZ) model which is a realistic traffic model, reliable for TSE and control. A state-space formulation is presented for the ARZ model considering junctions in the formulation which is important to model real highways with ramps. Linear approximation of the state-space model is investigated with respect to two techniques, first-order Taylor series approximation and Carleman linearization. A Moving Horizon Estimation (MHE) implementation is presented for TSE using a linearized ARZ model. Various state-estimation techniques used for TSE in the literature along with the presented approach are compared with regard to accuracy, computational tractability and parameter tuning with the help of a case study using the VISSIM traffic simulation software. Several research questions are posed and addressed with thorough analysis of the results.