A distributed local Kalman consensus filter for traffic estimation: design, analysis and validation

This thesis proposes a distributed local Kalman consensus filter (DLKCF) for large-scale multi-agent traffic density estimation. The switching mode model (SMM) describes 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, the estimates given by the DLKCF are proved to be ultimately bounded. We also show that under some frequently encountered conditions, the error sum in an unobservable section converges to a fixed value. Numerical experiments verify the asymptotic stability of the DLKCF for observable modes, compare the DLKCF to a Luenberger observer, illustrate the capability of the DLKCF on promoting consensus among various local agents, and show a considerable reduction of the runtime of the DLKCF compared to a central KF. Supplementary source code is available to be downloaded at https://github.com/yesun/DLKCFthesis.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Civil Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Sun, Ye

Contributors dc:contributor

• Work, Daniel B.

• Traffic state estimation
• hybrid systems
• observability
• distributed Kalman filter
• consensus filter

Statement dc:rights

• Copyright 2014 Ye Sun

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/72800

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/72800