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A probabilistic hypothesis density filter for traffic flow estimation in the presence of clutter

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A probabilistic hypothesis density filter for traffic flow estimation in the presence of clutter. / Canaud, Matthieu; Mihaylova, Lyudmila; El Faouzi, Nour-Eddin; Billot, Romain; Sau, Jacques.

Sensor Data Fusion: Trends, Solutions, Applications (SDF), 2012 Workshop on. IEEE, 2012. p. 31-36.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Canaud, M, Mihaylova, L, El Faouzi, N-E, Billot, R & Sau, J 2012, A probabilistic hypothesis density filter for traffic flow estimation in the presence of clutter. in Sensor Data Fusion: Trends, Solutions, Applications (SDF), 2012 Workshop on. IEEE, pp. 31-36. https://doi.org/10.1109/SDF.2012.6327904

APA

Canaud, M., Mihaylova, L., El Faouzi, N-E., Billot, R., & Sau, J. (2012). A probabilistic hypothesis density filter for traffic flow estimation in the presence of clutter. In Sensor Data Fusion: Trends, Solutions, Applications (SDF), 2012 Workshop on (pp. 31-36). IEEE. https://doi.org/10.1109/SDF.2012.6327904

Vancouver

Canaud M, Mihaylova L, El Faouzi N-E, Billot R, Sau J. A probabilistic hypothesis density filter for traffic flow estimation in the presence of clutter. In Sensor Data Fusion: Trends, Solutions, Applications (SDF), 2012 Workshop on. IEEE. 2012. p. 31-36 https://doi.org/10.1109/SDF.2012.6327904

Author

Canaud, Matthieu ; Mihaylova, Lyudmila ; El Faouzi, Nour-Eddin ; Billot, Romain ; Sau, Jacques. / A probabilistic hypothesis density filter for traffic flow estimation in the presence of clutter. Sensor Data Fusion: Trends, Solutions, Applications (SDF), 2012 Workshop on. IEEE, 2012. pp. 31-36

Bibtex

@inproceedings{445129d7bd044953ba03dede130b5945,
title = "A probabilistic hypothesis density filter for traffic flow estimation in the presence of clutter",
abstract = "Prediction of traffic flow variables such as traffic volume, travel speed or travel time for a short time horizon is of paramount importance in traffic control. Hence, the data assimilation process in traffic modeling for estimation and prediction plays a key role. However, the increasing complexity, non-linearity and presence of various uncertainties (both inthe measured data and models) are important factors affecting the traffic state prediction. To overcome this problem, new methodologies have been proposed. With this aim, in this paper we propose the use of the Probability Hypothesis Density (PHD) filter for traffic estimation. This methology is intensively studied, developed and improved for the purposes of multiple object tracking and consists in the recursive state estimation of several targets by using the information coming from an observation process. However, some issues need to be studied, especially theimpact of the clutter (false alarm) intensity. The goal of this paper is to expose the potential of the PHD filters for real-time traffic state estimation and the choice of an appropriate clutter intensity. This investigation is based on a Cell Transmission Model (CTM) coupled with the PHD filter. It brings a novel tool to the stateestimation problem and allows one to estimate the densities in traffic networks. In this work, we compare this PHD filter with the particle filter (PF) which has been successfully applied intraffic control and conclude that the PHD filter can be seen as a relevant alternative that opens new research avenues.",
keywords = "Vehicular traffic, traffic estimation, clutter, Probability hypothesis density filters, Real data, traffic modelling",
author = "Matthieu Canaud and Lyudmila Mihaylova and {El Faouzi}, Nour-Eddin and Romain Billot and Jacques Sau",
year = "2012",
month = sep,
day = "4",
doi = "10.1109/SDF.2012.6327904",
language = "English",
isbn = "9781467330107",
pages = "31--36",
booktitle = "Sensor Data Fusion: Trends, Solutions, Applications (SDF), 2012 Workshop on",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - A probabilistic hypothesis density filter for traffic flow estimation in the presence of clutter

AU - Canaud, Matthieu

AU - Mihaylova, Lyudmila

AU - El Faouzi, Nour-Eddin

AU - Billot, Romain

AU - Sau, Jacques

PY - 2012/9/4

Y1 - 2012/9/4

N2 - Prediction of traffic flow variables such as traffic volume, travel speed or travel time for a short time horizon is of paramount importance in traffic control. Hence, the data assimilation process in traffic modeling for estimation and prediction plays a key role. However, the increasing complexity, non-linearity and presence of various uncertainties (both inthe measured data and models) are important factors affecting the traffic state prediction. To overcome this problem, new methodologies have been proposed. With this aim, in this paper we propose the use of the Probability Hypothesis Density (PHD) filter for traffic estimation. This methology is intensively studied, developed and improved for the purposes of multiple object tracking and consists in the recursive state estimation of several targets by using the information coming from an observation process. However, some issues need to be studied, especially theimpact of the clutter (false alarm) intensity. The goal of this paper is to expose the potential of the PHD filters for real-time traffic state estimation and the choice of an appropriate clutter intensity. This investigation is based on a Cell Transmission Model (CTM) coupled with the PHD filter. It brings a novel tool to the stateestimation problem and allows one to estimate the densities in traffic networks. In this work, we compare this PHD filter with the particle filter (PF) which has been successfully applied intraffic control and conclude that the PHD filter can be seen as a relevant alternative that opens new research avenues.

AB - Prediction of traffic flow variables such as traffic volume, travel speed or travel time for a short time horizon is of paramount importance in traffic control. Hence, the data assimilation process in traffic modeling for estimation and prediction plays a key role. However, the increasing complexity, non-linearity and presence of various uncertainties (both inthe measured data and models) are important factors affecting the traffic state prediction. To overcome this problem, new methodologies have been proposed. With this aim, in this paper we propose the use of the Probability Hypothesis Density (PHD) filter for traffic estimation. This methology is intensively studied, developed and improved for the purposes of multiple object tracking and consists in the recursive state estimation of several targets by using the information coming from an observation process. However, some issues need to be studied, especially theimpact of the clutter (false alarm) intensity. The goal of this paper is to expose the potential of the PHD filters for real-time traffic state estimation and the choice of an appropriate clutter intensity. This investigation is based on a Cell Transmission Model (CTM) coupled with the PHD filter. It brings a novel tool to the stateestimation problem and allows one to estimate the densities in traffic networks. In this work, we compare this PHD filter with the particle filter (PF) which has been successfully applied intraffic control and conclude that the PHD filter can be seen as a relevant alternative that opens new research avenues.

KW - Vehicular traffic

KW - traffic estimation

KW - clutter

KW - Probability hypothesis density filters

KW - Real data

KW - traffic modelling

U2 - 10.1109/SDF.2012.6327904

DO - 10.1109/SDF.2012.6327904

M3 - Conference contribution/Paper

SN - 9781467330107

SP - 31

EP - 36

BT - Sensor Data Fusion: Trends, Solutions, Applications (SDF), 2012 Workshop on

PB - IEEE

ER -