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Probability hypothesis density filtering for real-time traffic state estimation and prediction

By Matthieu Canaud, Lyudmila Mihaylova, Jacques Sau and Nour-Eddin El Faouzi

Abstract

The probability hypothesis density (PHD) methodology is widely used by the research community for the purposes of multiple object tracking. This problem consists in the recursive state estimation of several targets by using the information coming from an observation process. The purpose of this paper is to investigate the potential of the PHD filters for real-time traffic state estimation. This investigation is based on a Cell Transmission Model (CTM) coupled with the PHD filter. It brings a novel tool to the state estimation problem and allows to estimate the densities in traffic networks in the presence of measurement origin uncertainty, detection uncertainty and noises. In this work, we compare the PHD filter performance with a particle filter (PF), both taking into account the measurement origin uncertainty and show that they can provide high accuracy in a traffic setting and real-time computational costs. The PHD filtering framework opens new research avenues and has the abilities to solve challenging problems of vehicular networks

Topics: Cell transmission model, Computational costs, Filtering framework, Measurement origin uncertainties, Multiple object tracking, Probability hypothesis density, Recursive state estimation, Research communities, State estimation, Target tracking, Uncertaint
Publisher: 'American Institute of Mathematical Sciences (AIMS)'
Year: 2013
DOI identifier: 10.3934/nhm.2013.8.825
OAI identifier: oai:eprints.qut.edu.au:101257
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