A distributed local Kalman consensus filter for traffic estimation

Ye Sun; Daniel B. Work

doi:10.1109/CDC.2014.7040406

A distributed local Kalman consensus filter for traffic estimation

Ye Sun, Daniel B. Work

Civil and Environmental Engineering

Research output: Contribution to journal › Conference article

Abstract

This work proposes a distributed local Kalman consensus filter (DLKCF) for large-scale multi-agent traffic density estimation. The switching mode model (SMM) is used to describe the traffic dynamics on a stretch of roadway, and the model dynamics are linear within each mode. The error dynamics of the proposed DLKCF is shown to be globally asymptotically stable (GAS) when all freeway sections switch between observable modes. For an unobservable section, we prove that the estimates given by the DLKCF are ultimately bounded. Numerical experiments are provided to show the asymptotic stability of the DLKCF for observable modes, and illustrate the effect of the DLKCF on promoting consensus among various local agents. Supplementary source code is available at https://github.com/yesun/DLKCFcdc2014.

Original language	English (US)
Article number	7040406
Pages (from-to)	6484-6491
Number of pages	8
Journal	Proceedings of the IEEE Conference on Decision and Control
Volume	2015-February
Issue number	February
DOIs	https://doi.org/10.1109/CDC.2014.7040406
State	Published - Jan 1 2014
Event	2014 53rd IEEE Annual Conference on Decision and Control, CDC 2014 - Los Angeles, United States Duration: Dec 15 2014 → Dec 17 2014

ASJC Scopus subject areas

Control and Systems Engineering
Modeling and Simulation
Control and Optimization

Access to Document

10.1109/CDC.2014.7040406

Fingerprint Dive into the research topics of 'A distributed local Kalman consensus filter for traffic estimation'. Together they form a unique fingerprint.

Cite this

Sun, Y., & Work, D. B. (2014). A distributed local Kalman consensus filter for traffic estimation. Proceedings of the IEEE Conference on Decision and Control, 2015-February(February), 6484-6491. [7040406]. https://doi.org/10.1109/CDC.2014.7040406

@article{a7e6d7e14f054f07bc95a6f01374447d,

title = "A distributed local Kalman consensus filter for traffic estimation",

abstract = "This work proposes a distributed local Kalman consensus filter (DLKCF) for large-scale multi-agent traffic density estimation. The switching mode model (SMM) is used to describe the traffic dynamics on a stretch of roadway, and the model dynamics are linear within each mode. The error dynamics of the proposed DLKCF is shown to be globally asymptotically stable (GAS) when all freeway sections switch between observable modes. For an unobservable section, we prove that the estimates given by the DLKCF are ultimately bounded. Numerical experiments are provided to show the asymptotic stability of the DLKCF for observable modes, and illustrate the effect of the DLKCF on promoting consensus among various local agents. Supplementary source code is available at https://github.com/yesun/DLKCFcdc2014.",

author = "Ye Sun and Work, {Daniel B.}",

year = "2014",

month = jan,

day = "1",

doi = "10.1109/CDC.2014.7040406",

language = "English (US)",

volume = "2015-February",

pages = "6484--6491",

journal = "Proceedings of the IEEE Conference on Decision and Control",

issn = "0191-2216",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

number = "February",

note = "2014 53rd IEEE Annual Conference on Decision and Control, CDC 2014 ; Conference date: 15-12-2014 Through 17-12-2014",

}

TY - JOUR

T1 - A distributed local Kalman consensus filter for traffic estimation

AU - Sun, Ye

AU - Work, Daniel B.

PY - 2014/1/1

Y1 - 2014/1/1

N2 - This work proposes a distributed local Kalman consensus filter (DLKCF) for large-scale multi-agent traffic density estimation. The switching mode model (SMM) is used to describe the traffic dynamics on a stretch of roadway, and the model dynamics are linear within each mode. The error dynamics of the proposed DLKCF is shown to be globally asymptotically stable (GAS) when all freeway sections switch between observable modes. For an unobservable section, we prove that the estimates given by the DLKCF are ultimately bounded. Numerical experiments are provided to show the asymptotic stability of the DLKCF for observable modes, and illustrate the effect of the DLKCF on promoting consensus among various local agents. Supplementary source code is available at https://github.com/yesun/DLKCFcdc2014.

AB - This work proposes a distributed local Kalman consensus filter (DLKCF) for large-scale multi-agent traffic density estimation. The switching mode model (SMM) is used to describe the traffic dynamics on a stretch of roadway, and the model dynamics are linear within each mode. The error dynamics of the proposed DLKCF is shown to be globally asymptotically stable (GAS) when all freeway sections switch between observable modes. For an unobservable section, we prove that the estimates given by the DLKCF are ultimately bounded. Numerical experiments are provided to show the asymptotic stability of the DLKCF for observable modes, and illustrate the effect of the DLKCF on promoting consensus among various local agents. Supplementary source code is available at https://github.com/yesun/DLKCFcdc2014.

UR - http://www.scopus.com/inward/record.url?scp=84961991063&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84961991063&partnerID=8YFLogxK

U2 - 10.1109/CDC.2014.7040406

DO - 10.1109/CDC.2014.7040406

M3 - Conference article

AN - SCOPUS:84961991063

VL - 2015-February

SP - 6484

EP - 6491

JO - Proceedings of the IEEE Conference on Decision and Control

JF - Proceedings of the IEEE Conference on Decision and Control

SN - 0191-2216

IS - February

M1 - 7040406

T2 - 2014 53rd IEEE Annual Conference on Decision and Control, CDC 2014

Y2 - 15 December 2014 through 17 December 2014

ER -