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

Matthieu Canaud; Lyudmila Mihaylova; Jacques Sau; Nour-Eddin El Faouzi; Matthieu Canaud; Lyudmila Mihaylova; Jacques Sau; Nour-Eddin El Faouzi

doi:10.3934/nhm.2013.8.825

Networks and Heterogeneous Media

2013, Volume 8, Issue 3: 825-842. doi: 10.3934/nhm.2013.8.825

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

1.
Université de Lyon, F-69000, Lyon
2.
Department of Automatic Control and Systems Engineering, Mappin Street, University of Sheffield, Sheffield S1 3JD

Received: 01 April 2012 Revised: 01 June 2013
Primary: 60J10, 60J20; Secondary: 60E05, 62L12.

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.
- Traffic control,
- particle filter,
- probability hypothesis density filter,
- traffic state estimation,
- sequential Monte Carlo methods
Citation: Matthieu Canaud, Lyudmila Mihaylova, Jacques Sau, Nour-Eddin El Faouzi. Probability hypothesis density filtering for real-time traffic state estimation and prediction[J]. Networks and Heterogeneous Media, 2013, 8(3): 825-842. doi: 10.3934/nhm.2013.8.825

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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.

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