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发表于 2005-10-10 16:53:44
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Motorway network thraffic control systems
Real-time freeway traffic state estimation based on extended Kalman filter: a general approach
Yibing Wang *, Markos Papageorgiou
Dynamic Systems and Simulation Laboratory, Technical University of Crete, 73100 Chania, Greece
Received 18 November 2002; received in revised form 25 February 2003; accepted 1 March 2004
Available online
Abstract
A general approach to the real-time estimation of the complete traffic state in freeway stretches is
developed based on the extended Kalman filter. First, a general stochastic macroscopic traffic flow model of
freeway stretches is presented, while some simple formulae are proposed to model real-time traffic measurements.
Second, the macroscopic traffic flow model along with the measurement model is organized in a
compact state-space form, based on which a traffic state estimator is designed by use of the extended-
Kalman-filtering method. While constructing the traffic state estimator, special attention is paid to the
handling of the boundary conditions and unknown parameters of the macroscopic traffic flow model. A
number of simulations are conducted to test the designed traffic state estimator under various traffic situations
in a freeway stretch with on/off-ramps and a long inter-detector distance. Some key issues are
carefully investigated, including tracking capability of the traffic state estimator, comparison of various
estimation schemes, evaluation of different detector configurations, significance of the on-line model
parameter estimation, sensitivity of the traffic state estimator to the initial values of the estimated model
parameters and to the related standard deviation values, and dynamic tracking of time-varying model
parameters. The achieved simulation results are very promising for the subsequent development and testing
work that is briefly outlined.
2004 Elsevier Ltd. All rights reserved.
Keywords: Freeway; Traffic state estimation; Stochastic macroscopic traffic flow model; Extended Kalman filter; Model
parameter estimation
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