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Parallelised Gaussian mixture filtering for vehicular traffic flow estimation.

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Parallelised Gaussian mixture filtering for vehicular traffic flow estimation. / Mihaylova, Lyudmila; Gning, Amadou; Doychinov, V. et al.
Lecture Notes in Informatics. ed. / S. Fischer; E. Maehle; R. Reischuk. P-154. ed. Germany: Luebeck, 2009. p. 2321-2333 (Proceedings Series of the Gesellschaft fur Informatik).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter

Harvard

Mihaylova, L, Gning, A, Doychinov, V & Boel, R 2009, Parallelised Gaussian mixture filtering for vehicular traffic flow estimation. in S Fischer, E Maehle & R Reischuk (eds), Lecture Notes in Informatics. P-154 edn, Proceedings Series of the Gesellschaft fur Informatik, Luebeck, Germany, pp. 2321-2333. <http://www.informatik2009.de/workshops0.html>

APA

Mihaylova, L., Gning, A., Doychinov, V., & Boel, R. (2009). Parallelised Gaussian mixture filtering for vehicular traffic flow estimation. In S. Fischer, E. Maehle, & R. Reischuk (Eds.), Lecture Notes in Informatics (P-154 ed., pp. 2321-2333). (Proceedings Series of the Gesellschaft fur Informatik). Luebeck. http://www.informatik2009.de/workshops0.html

Vancouver

Mihaylova L, Gning A, Doychinov V, Boel R. Parallelised Gaussian mixture filtering for vehicular traffic flow estimation. In Fischer S, Maehle E, Reischuk R, editors, Lecture Notes in Informatics. P-154 ed. Germany: Luebeck. 2009. p. 2321-2333. (Proceedings Series of the Gesellschaft fur Informatik).

Author

Mihaylova, Lyudmila ; Gning, Amadou ; Doychinov, V. et al. / Parallelised Gaussian mixture filtering for vehicular traffic flow estimation. Lecture Notes in Informatics. editor / S. Fischer ; E. Maehle ; R. Reischuk. P-154. ed. Germany : Luebeck, 2009. pp. 2321-2333 (Proceedings Series of the Gesellschaft fur Informatik).

Bibtex

@inbook{cfac7d05fafa43bc87b9c3a842740d75,
title = "Parallelised Gaussian mixture filtering for vehicular traffic flow estimation.",
abstract = "Large traffic network systems require handling huge amounts of data, often distributed over a large geographical region in space and time. Centralised processing is not then the right choice in such cases. In this paper we develop a parallelised Gaussian Mixture Model filter (GMMF) for traffic networks aimed to: 1) work with high amounts of data and heterogenous data (from different sensor modalities), 2) provide robustness in the presence of sparse and missing sensor data, 3) able to incorporate different models in different traffic segments and represent various traffic regimes, 4) able to cope with multimodalities (e.g., due to multimodal measurement likelihood or multimodal state probability density functions). The efficiency of the parallelised GMMF is investigated over traffic flows based on macroscopic modelling and compared with a centralised GMMF. The proposed GMM approach is general, it is applicable to systems where the overall state vector can be partitioned into state components (subsets), corresponding to certain geographical regions, such that most of the interactions take place within the subsets. The performance of the paralellised and centralised GMMFs is investigated and evaluated in terms of accuracy and complexity.",
keywords = "Parallelised Gaussian Mixture filters, vehicular traffic state estimation, multimodal probability density function",
author = "Lyudmila Mihaylova and Amadou Gning and V. Doychinov and R. Boel",
note = "Published in Lecture Notes in Informatics, pp. 2321–2333, Volume P-154, Volume P-154, ISBN 978-3-88579-248-2 ISSN 1617-5468",
year = "2009",
month = oct,
day = "1",
language = "English",
series = "Proceedings Series of the Gesellschaft fur Informatik",
publisher = "Luebeck",
pages = "2321--2333",
editor = "S. Fischer and E. Maehle and R. Reischuk",
booktitle = "Lecture Notes in Informatics",
edition = "P-154",

}

RIS

TY - CHAP

T1 - Parallelised Gaussian mixture filtering for vehicular traffic flow estimation.

AU - Mihaylova, Lyudmila

AU - Gning, Amadou

AU - Doychinov, V.

AU - Boel, R.

N1 - Published in Lecture Notes in Informatics, pp. 2321–2333, Volume P-154, Volume P-154, ISBN 978-3-88579-248-2 ISSN 1617-5468

PY - 2009/10/1

Y1 - 2009/10/1

N2 - Large traffic network systems require handling huge amounts of data, often distributed over a large geographical region in space and time. Centralised processing is not then the right choice in such cases. In this paper we develop a parallelised Gaussian Mixture Model filter (GMMF) for traffic networks aimed to: 1) work with high amounts of data and heterogenous data (from different sensor modalities), 2) provide robustness in the presence of sparse and missing sensor data, 3) able to incorporate different models in different traffic segments and represent various traffic regimes, 4) able to cope with multimodalities (e.g., due to multimodal measurement likelihood or multimodal state probability density functions). The efficiency of the parallelised GMMF is investigated over traffic flows based on macroscopic modelling and compared with a centralised GMMF. The proposed GMM approach is general, it is applicable to systems where the overall state vector can be partitioned into state components (subsets), corresponding to certain geographical regions, such that most of the interactions take place within the subsets. The performance of the paralellised and centralised GMMFs is investigated and evaluated in terms of accuracy and complexity.

AB - Large traffic network systems require handling huge amounts of data, often distributed over a large geographical region in space and time. Centralised processing is not then the right choice in such cases. In this paper we develop a parallelised Gaussian Mixture Model filter (GMMF) for traffic networks aimed to: 1) work with high amounts of data and heterogenous data (from different sensor modalities), 2) provide robustness in the presence of sparse and missing sensor data, 3) able to incorporate different models in different traffic segments and represent various traffic regimes, 4) able to cope with multimodalities (e.g., due to multimodal measurement likelihood or multimodal state probability density functions). The efficiency of the parallelised GMMF is investigated over traffic flows based on macroscopic modelling and compared with a centralised GMMF. The proposed GMM approach is general, it is applicable to systems where the overall state vector can be partitioned into state components (subsets), corresponding to certain geographical regions, such that most of the interactions take place within the subsets. The performance of the paralellised and centralised GMMFs is investigated and evaluated in terms of accuracy and complexity.

KW - Parallelised Gaussian Mixture filters

KW - vehicular traffic state estimation

KW - multimodal probability density function

M3 - Chapter

T3 - Proceedings Series of the Gesellschaft fur Informatik

SP - 2321

EP - 2333

BT - Lecture Notes in Informatics

A2 - Fischer, S.

A2 - Maehle, E.

A2 - Reischuk, R.

PB - Luebeck

CY - Germany

ER -