An Improved Macroscopic Modeling for Highway Traffic Density Estimation

Abstract

Efficient and accurate estimation of traffic density plays an important role in the development of intelligent transportation systems by providing relevant information for rapid decision-making. The purpose of this study is to design a model-based procedure to estimate traffic density. Here, we design an innovative observer that combines the benefits of piecewise switched linear traffic model with Luenberger observer estimator for improving road traffic density estimation. We evaluated the proposed estimator by using traffic data from the four-lane SR-60 freeway in southern California.