HIGHWAY TRAFFIC STATE ESTIMATION USING IMPROVED MIXTURE KALMAN FILTERS FOR EFFECTIVE RAMP METERING CONTROL
A cell transmission model-based switching state-space model was used to estimate vehicle densities and congestion modes at unmeasured locations on a highway section. The mixture Kalman filter algorithm, based on a sequential Monte Carlo method, is used to approximately solve the difficult problem of inference on a switching state-space model with an unobserved discrete state. The authors propose a scheme to prevent the risk of weight underflow and to introduce forgetting. Estimation results show that comparable accuracies can be achieved using either a small or large number of sampling sequences, thus making it possible to carry out efficient online filtering. Underflow prevention and forgetting improves estimation accuracy in the examples provided. On average, a mean percentage error of approximately 10% is achieved for the vehicle density estimation. The estimation performance is consistent with data sets from various days.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/isbn/0780379241
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Corporate Authors:
Institute of Electrical and Electronics Engineers (IEEE)
3 Park Avenue, 17th Floor
New York, NY United States 10016-5997 -
Authors:
- Sun, X Q
- Munoz, L
- Horowitz, R
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Conference:
- 42nd IEEE International Conference on Decision and Control
- Location: Maui, Hawaii
- Date: 2003-12-9 to 2003-12-12
- Publication Date: 2003
Language
- English
Media Info
- Features: References;
- Pagination: p. 6333-38
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Serial:
- Volume: 3
Subject/Index Terms
- TRT Terms: Algorithms; Interchange ramps; Kalman filtering; Monte Carlo method; Ramp metering; Traffic density; Traffic engineering; Traffic estimation; Traffic models
- Subject Areas: Highways; Operations and Traffic Management; I73: Traffic Control;
Filing Info
- Accession Number: 00979110
- Record Type: Publication
- ISBN: 0780379241
- Files: TRIS
- Created Date: Sep 17 2004 12:00AM