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Brief paper: Freeway traffic estimation within particle filtering framework

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

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Brief paper: Freeway traffic estimation within particle filtering framework. / Mihaylova, Lyudmila; Boel, René; Hegyi, Andreas.
In: Automatica, Vol. 43, No. 2, 2007, p. 290-300.

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

Harvard

Mihaylova, L, Boel, R & Hegyi, A 2007, 'Brief paper: Freeway traffic estimation within particle filtering framework', Automatica, vol. 43, no. 2, pp. 290-300. https://doi.org/10.1016/j.automatica.2006.08.023

APA

Mihaylova, L., Boel, R., & Hegyi, A. (2007). Brief paper: Freeway traffic estimation within particle filtering framework. Automatica, 43(2), 290-300. https://doi.org/10.1016/j.automatica.2006.08.023

Vancouver

Mihaylova L, Boel R, Hegyi A. Brief paper: Freeway traffic estimation within particle filtering framework. Automatica. 2007;43(2):290-300. doi: 10.1016/j.automatica.2006.08.023

Author

Mihaylova, Lyudmila ; Boel, René ; Hegyi, Andreas. / Brief paper: Freeway traffic estimation within particle filtering framework. In: Automatica. 2007 ; Vol. 43, No. 2. pp. 290-300.

Bibtex

@article{3d915e789d484cdf82e8271d3fb3cf4a,
title = "Brief paper: Freeway traffic estimation within particle filtering framework",
abstract = "This paper formulates the problem of real-time estimation of traffic state in freeway networks by means of the particle filtering framework. A particle filter (PF) is developed based on a recently proposed speed-extended cell-transmission model of freeway traffic. The freeway is considered as a network of components representing different freeway stretches called segments. The evolution of the traffic in a segment is modelled as a dynamic stochastic system, influenced by states of neighbour segments. Measurements are received only at boundaries between some segments and averaged within possibly irregular time intervals. This limits the measurement update in the PF to only these time instants when a new measurement arrives, while in between measurement updates any simulation model can be used to describe the evolution of the particles. The PF performance is validated and evaluated using synthetic and real traffic data from a Belgian freeway. An unscented Kalman filter is also presented. A comparison of the PF with the unscented Kalman filter is performed with respect to accuracy and complexity.",
keywords = "Bayesian estimation, Particle filtering, Macroscopic traffic models, Stochastic systems, Unscented Kalman filter, DCS-publications-id, art-827, DCS-publications-credits, dsp-fa, DCS-publications-personnel-id, 121",
author = "Lyudmila Mihaylova and Ren{\'e} Boel and Andreas Hegyi",
note = "“The final, definitive version of this article has been published in the Journal, Automatica, 43 (2), 2007, {\textcopyright} ELSEVIER.",
year = "2007",
doi = "10.1016/j.automatica.2006.08.023",
language = "English",
volume = "43",
pages = "290--300",
journal = "Automatica",
issn = "0005-1098",
publisher = "Elsevier Limited",
number = "2",

}

RIS

TY - JOUR

T1 - Brief paper: Freeway traffic estimation within particle filtering framework

AU - Mihaylova, Lyudmila

AU - Boel, René

AU - Hegyi, Andreas

N1 - “The final, definitive version of this article has been published in the Journal, Automatica, 43 (2), 2007, © ELSEVIER.

PY - 2007

Y1 - 2007

N2 - This paper formulates the problem of real-time estimation of traffic state in freeway networks by means of the particle filtering framework. A particle filter (PF) is developed based on a recently proposed speed-extended cell-transmission model of freeway traffic. The freeway is considered as a network of components representing different freeway stretches called segments. The evolution of the traffic in a segment is modelled as a dynamic stochastic system, influenced by states of neighbour segments. Measurements are received only at boundaries between some segments and averaged within possibly irregular time intervals. This limits the measurement update in the PF to only these time instants when a new measurement arrives, while in between measurement updates any simulation model can be used to describe the evolution of the particles. The PF performance is validated and evaluated using synthetic and real traffic data from a Belgian freeway. An unscented Kalman filter is also presented. A comparison of the PF with the unscented Kalman filter is performed with respect to accuracy and complexity.

AB - This paper formulates the problem of real-time estimation of traffic state in freeway networks by means of the particle filtering framework. A particle filter (PF) is developed based on a recently proposed speed-extended cell-transmission model of freeway traffic. The freeway is considered as a network of components representing different freeway stretches called segments. The evolution of the traffic in a segment is modelled as a dynamic stochastic system, influenced by states of neighbour segments. Measurements are received only at boundaries between some segments and averaged within possibly irregular time intervals. This limits the measurement update in the PF to only these time instants when a new measurement arrives, while in between measurement updates any simulation model can be used to describe the evolution of the particles. The PF performance is validated and evaluated using synthetic and real traffic data from a Belgian freeway. An unscented Kalman filter is also presented. A comparison of the PF with the unscented Kalman filter is performed with respect to accuracy and complexity.

KW - Bayesian estimation

KW - Particle filtering

KW - Macroscopic traffic models

KW - Stochastic systems

KW - Unscented Kalman filter

KW - DCS-publications-id

KW - art-827

KW - DCS-publications-credits

KW - dsp-fa

KW - DCS-publications-personnel-id

KW - 121

U2 - 10.1016/j.automatica.2006.08.023

DO - 10.1016/j.automatica.2006.08.023

M3 - Journal article

VL - 43

SP - 290

EP - 300

JO - Automatica

JF - Automatica

SN - 0005-1098

IS - 2

ER -