Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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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 -