01154398

Component

https://doi.org/10.3141/2188-13

http://scholar.google.com...alee&publication_year=2010

Traffic state estimation problems must deal with imperfect information. Imperfect information may occur as a result of misalignments between reality and the assumptions or simplifications made when developing the models as well as incomplete or corrupted data. One of the well-known methods of handling imperfect information is the Kalman filter algorithm and its variations, such as the extended Kalman filter and the unscented Kalman filter. While the standard Kalman filter and the extended Kalman filter have been widely applied to the state estimation of linear systems, the unscented Kalman filter has been reported the better choice in the multiclass traffic state estimation. In many applications of Kalman filters to traffic estimation problems, the model and measurement noise covariance matrices are normally estimated. When there is a mismatch between the true and the assumed noise distribution, however, the filter often suffers from performance degradation and even divergence in certain situations. To this end, this paper presents a more efficient and accurate algorithm embedded in the unscented Kalman filter to simultaneously estimate the traffic state and the model noise distribution statistics. The proposed method is facilitated through a simultaneous update of the model noise covariance matrix in the predicted covariance equations of the unscented Kalman filter algorithm. It is found through simulation that the proposed algorithm may improve the model performance over the standard algorithm.

01329026

10-2265

English

pp 119-130

2010

Transportation Research Record: Journal of the Transportation Research Board

9780309160629

Print

Figures (8) ; References (16) ; Tables (1)

Algorithms; Kalman filtering; Traffic flow; Traffic models

Noise covariance matrices; Traffic state estimation

Highways; Operations and Traffic Management; I71: Traffic Theory

TRIS, TRB, ATRI

Jan 25 2010 11:02AM

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