Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Published

**Parallelized particle filtering for freeway traffic state tracking.** / Hegiy, A.; Mihaylova, L.; Boel, R.; Lendek, Z.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Hegiy, A, Mihaylova, L, Boel, R & Lendek, Z 2007, Parallelized particle filtering for freeway traffic state tracking. in *Proceedings of the European Control Conference 2007, Kos, Greece, 2-5 July 2007.* European Union Control Association, pp. 2442-2449, European Control Conference, Kos, Greece, 2/07/07. <http://ecc07.ntua.gr>

Hegiy, A., Mihaylova, L., Boel, R., & Lendek, Z. (2007). Parallelized particle filtering for freeway traffic state tracking. In *Proceedings of the European Control Conference 2007, Kos, Greece, 2-5 July 2007 *(pp. 2442-2449). European Union Control Association. http://ecc07.ntua.gr

Hegiy A, Mihaylova L, Boel R, Lendek Z. Parallelized particle filtering for freeway traffic state tracking. In Proceedings of the European Control Conference 2007, Kos, Greece, 2-5 July 2007. European Union Control Association. 2007. p. 2442-2449

@inproceedings{685d38cc54d84b6baa7f4a79630fa2cb,

title = "Parallelized particle filtering for freeway traffic state tracking",

abstract = "We consider parallelized particle filters for state tracking (estimation) of freeway traffic networks. Particle filters can accurately solve the state estimation problem for general nonlinear systems with non-Gaussian noises. However, this high accuracy may come at the cost of high computational demand. We present two parallelized particle filtering algorithms where the calculations are divided over several processing units (PUs) which reduces the computational demand per processing unit. Existing parallelization approaches typically assign sets of particles to PUs such that each full particle resides at one PU. In contrast, we partition each particle according to a partitioning of the network into subnetworks based on the topology of the network. The centralized case and the two proposed approaches are evaluated with a benchmark problem by comparing the estimation accuracy, computational complexity and communication needs. This approach is in general applicable to systems where it is possible to partition the overall state into subsets of states, such that most of the interaction takes place within the subsets. Keywords: Parallel particle filters, freeway traffic state tracking.",

keywords = "Parallel particle filters, freeway traffic state tracking. DCS-publications-id, 121",

author = "A. Hegiy and L. Mihaylova and R. Boel and Z. Lendek",

year = "2007",

month = jul,

language = "English",

isbn = "978-960-89028-5-5",

pages = "2442--2449",

booktitle = "Proceedings of the European Control Conference 2007, Kos, Greece, 2-5 July 2007",

publisher = "European Union Control Association",

note = "European Control Conference ; Conference date: 02-07-2007 Through 05-07-2007",

}

TY - GEN

T1 - Parallelized particle filtering for freeway traffic state tracking

AU - Hegiy, A.

AU - Mihaylova, L.

AU - Boel, R.

AU - Lendek, Z.

PY - 2007/7

Y1 - 2007/7

N2 - We consider parallelized particle filters for state tracking (estimation) of freeway traffic networks. Particle filters can accurately solve the state estimation problem for general nonlinear systems with non-Gaussian noises. However, this high accuracy may come at the cost of high computational demand. We present two parallelized particle filtering algorithms where the calculations are divided over several processing units (PUs) which reduces the computational demand per processing unit. Existing parallelization approaches typically assign sets of particles to PUs such that each full particle resides at one PU. In contrast, we partition each particle according to a partitioning of the network into subnetworks based on the topology of the network. The centralized case and the two proposed approaches are evaluated with a benchmark problem by comparing the estimation accuracy, computational complexity and communication needs. This approach is in general applicable to systems where it is possible to partition the overall state into subsets of states, such that most of the interaction takes place within the subsets. Keywords: Parallel particle filters, freeway traffic state tracking.

AB - We consider parallelized particle filters for state tracking (estimation) of freeway traffic networks. Particle filters can accurately solve the state estimation problem for general nonlinear systems with non-Gaussian noises. However, this high accuracy may come at the cost of high computational demand. We present two parallelized particle filtering algorithms where the calculations are divided over several processing units (PUs) which reduces the computational demand per processing unit. Existing parallelization approaches typically assign sets of particles to PUs such that each full particle resides at one PU. In contrast, we partition each particle according to a partitioning of the network into subnetworks based on the topology of the network. The centralized case and the two proposed approaches are evaluated with a benchmark problem by comparing the estimation accuracy, computational complexity and communication needs. This approach is in general applicable to systems where it is possible to partition the overall state into subsets of states, such that most of the interaction takes place within the subsets. Keywords: Parallel particle filters, freeway traffic state tracking.

KW - Parallel particle filters

KW - freeway traffic state tracking. DCS-publications-id

KW - 121

M3 - Conference contribution/Paper

SN - 978-960-89028-5-5

SP - 2442

EP - 2449

BT - Proceedings of the European Control Conference 2007, Kos, Greece, 2-5 July 2007

PB - European Union Control Association

T2 - European Control Conference

Y2 - 2 July 2007 through 5 July 2007

ER -