DocumentCode :
974142
Title :
Nonlinear Kalman Filtering Algorithms for On-Line Calibration of Dynamic Traffic Assignment Models
Author :
Antoniou, Constantinos ; Ben-Akiva, Moshe ; Koutsopoulos, Haris N.
Author_Institution :
Massachusetts Inst. of Technol., Cambridge
Volume :
8
Issue :
4
fYear :
2007
Firstpage :
661
Lastpage :
670
Abstract :
An online calibration approach that jointly estimates demand and supply parameters of dynamic traffic assignment (DTA) systems is presented and empirically validated through an extensive application. The problem can be formulated as a nonlinear state-space model. Because of its nonlinear nature, the resulting model cannot be solved by the Kalman filter, and therefore, nonlinear extensions need to be considered. The following three extensions to the Kalman filtering algorithm are presented: 1) the extended Kalman filter (EKF); 2) the limiting EKF (LimEKF); and 3) the unscented Kalman filter. The solution algorithms are applied to the on-line calibration of the state-of-the-art DynaMIT DTA model, and their use is demonstrated in a freeway network in Southampton, U.K. The LimEKF shows accuracy that is comparable to that of the best algorithm but with vastly superior computational performance. The robustness of the approach to varying weather conditions is demonstrated, and practical aspects are discussed.
Keywords :
Kalman filters; calibration; nonlinear filters; road traffic; traffic control; demand parameter; dynamic traffic assignment model; extended Kalman filter; freeway network; limiting extended Kalman filter; nonlinear Kalman filtering algorithm; nonlinear state-space model; online calibration; supply parameter; unscented Kalman filter; Calibration; Communication system traffic control; Control systems; Disaster management; Filtering algorithms; Kalman filters; Predictive models; Real time systems; Telecommunication traffic; Traffic control; Dynamic traffic assignment (DTA); extended Kalman filter (EKF); limiting extended Kalman filter (LimEKF); nonlinear optimization; on-line calibration; unscented Kalman filter (UKF);
fLanguage :
English
Journal_Title :
Intelligent Transportation Systems, IEEE Transactions on
Publisher :
ieee
ISSN :
1524-9050
Type :
jour
DOI :
10.1109/TITS.2007.908569
Filename :
4382935
Link To Document :
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