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

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Probability hypothesis density filtering for real-time traffic state estimation and prediction. / Canaud, Matthieu; Mihaylova, Lyudmila; Sau, Jacques; El Faouzi, Nour-Eddin.

In: Network and Heterogeneous Media, Vol. 8, No. 3, 09.2013, p. 825-842.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Canaud, M, Mihaylova, L, Sau, J & El Faouzi, N-E 2013, 'Probability hypothesis density filtering for real-time traffic state estimation and prediction', Network and Heterogeneous Media, vol. 8, no. 3, pp. 825-842. https://doi.org/10.3934/nhm.2013.8.825

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Author

Canaud, Matthieu ; Mihaylova, Lyudmila ; Sau, Jacques ; El Faouzi, Nour-Eddin. / Probability hypothesis density filtering for real-time traffic state estimation and prediction. In: Network and Heterogeneous Media. 2013 ; Vol. 8, No. 3. pp. 825-842.

Bibtex

@article{d03b98eae5a7401097c6dbd7348a3d79,
title = "Probability hypothesis density filtering for real-time traffic state estimation and prediction",
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.",
keywords = "traffic, modelling, estimation, control, Probability hypothesis density filters, macroscopic models",
author = "Matthieu Canaud and Lyudmila Mihaylova and Jacques Sau and {El Faouzi}, Nour-Eddin",
note = "Special Issue on: Mathematics of Traffic Modelling, Estimation and Control",
year = "2013",
month = sep,
doi = "10.3934/nhm.2013.8.825",
language = "English",
volume = "8",
pages = "825--842",
journal = "Network and Heterogeneous Media",
issn = "1556-1801",
publisher = "American Institute of Mathematical Sciences",
number = "3",

}

RIS

TY - JOUR

T1 - Probability hypothesis density filtering for real-time traffic state estimation and prediction

AU - Canaud, Matthieu

AU - Mihaylova, Lyudmila

AU - Sau, Jacques

AU - El Faouzi, Nour-Eddin

N1 - Special Issue on: Mathematics of Traffic Modelling, Estimation and Control

PY - 2013/9

Y1 - 2013/9

N2 - 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.

AB - 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.

KW - traffic

KW - modelling

KW - estimation

KW - control

KW - Probability hypothesis density filters

KW - macroscopic models

U2 - 10.3934/nhm.2013.8.825

DO - 10.3934/nhm.2013.8.825

M3 - Journal article

VL - 8

SP - 825

EP - 842

JO - Network and Heterogeneous Media

JF - Network and Heterogeneous Media

SN - 1556-1801

IS - 3

ER -