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

TL;DR

This paper develops a traffic state estimation method using the second-order Aw-Rascle-Zhang model and connected vehicle data, improving accuracy and efficiency in highway traffic monitoring.

Contribution

It introduces a state-space formulation with junction modeling and applies Moving Horizon Estimation to enhance traffic state estimation using CV data.

Findings

The proposed method outperforms traditional approaches in accuracy.

Inclusion of junction modeling improves estimation reliability.

Different CV data querying strategies significantly affect estimation performance.

Abstract

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. A Moving Horizon Estimation (MHE) implementation is presented for TSE using a linearized ARZ model. Various state-estimation methods used for TSE in the literature along with the presented approach are compared with regard to accuracy and computational tractability with the help of a numerical study using the VISSIM traffic simulation software. The impact of various strategies for querying CV data on the estimation performance is also considered. Several research questions are posed and addressed with a thorough analysis of the results.