Decentralized State-Observer-Based Traffic Density Estimation of Large-Scale Urban Freeway Network by Dynamic Model

Yuqi Guo,Yangzhou Chen,Chiyuan Zhang

doi:10.3390/info8030095

Abstract

In order to estimate traffic densities in a large-scale urban freeway network in an accurate and timely fashion when traffic sensors do not cover the freeway network completely and thus only local measurement data can be utilized, this paper proposes a decentralized state observer approach based on a macroscopic traffic flow model. Firstly, by using the well-known cell transmission model (CTM), the urban freeway network is modeled in the way of distributed systems. Secondly, based on the model, a decentralized observer is designed. With the help of the Lyapunov function and S-procedure theory, the observer gains are computed by using linear matrix inequality (LMI) technique. So, the traffic densities of the whole road network can be estimated by the designed observer. Finally, this method is applied to the outer ring of the Beijing’s second ring road and experimental results demonstrate the effectiveness and applicability of the proposed approach.

Highlights

As one of the most important parameters of traffic flow and a key index to identify traffic state, traffic density estimation remains one of the major concerns in urban traffic networks
Muñoz and Sun et al presented the switching mode model (SMM) based on the cell transmission model (CTM) which is well suited for model-based traffic density estimation [3], and they further applied a semi-automated method to the California Freeway [4]
Other approaches were studied based on the dynamic model, as the efficient practical tools, based-model state observer and Kalman filter were utilized in traffic state estimation

Summary

Introduction

As one of the most important parameters of traffic flow and a key index to identify traffic state, traffic density estimation remains one of the major concerns in urban traffic networks. Various strategies have been proposed for tackling related problems in traffic density estimation Most of these methods are based on the use of a macroscopic traffic flow model, since Daganzo proposed the well-known cell transmission model (CTM) [1,2]. Muñoz and Sun et al presented the switching mode model (SMM) based on the CTM which is well suited for model-based traffic density estimation [3], and they further applied a semi-automated method to the California Freeway [4]. Chen and Guo et al [8,9] deduced a piecewise affine linear system (PWALS) by combining the cell transmission model (CTM) with the dynamic

Objectives

Results

Conclusion