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Fuzzy State Transition and Kalman Filter Applied in Short-Term Traffic Flow Forecasting
Joint Authors
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-12-08
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
Traffic flow is widely recognized as an important parameter for road traffic state forecasting.
Fuzzy state transform and Kalman filter (KF) have been applied in this field separately.
But the studies show that the former method has good performance on the trend forecasting of traffic state variation but always involves several numerical errors.
The latter model is good at numerical forecasting but is deficient in the expression of time hysteretically.
This paper proposed an approach that combining fuzzy state transform and KF forecasting model.
In considering the advantage of the two models, a weight combination model is proposed.
The minimum of the sum forecasting error squared is regarded as a goal in optimizing the combined weight dynamically.
Real detection data are used to test the efficiency.
Results indicate that the method has a good performance in terms of short-term traffic forecasting.
American Psychological Association (APA)
Ming-jun, Deng& Shi-ru, Qu. 2015. Fuzzy State Transition and Kalman Filter Applied in Short-Term Traffic Flow Forecasting. Computational Intelligence and Neuroscience،Vol. 2015, no. 2015, pp.1-7.
https://search.emarefa.net/detail/BIM-1057774
Modern Language Association (MLA)
Ming-jun, Deng& Shi-ru, Qu. Fuzzy State Transition and Kalman Filter Applied in Short-Term Traffic Flow Forecasting. Computational Intelligence and Neuroscience No. 2015 (2015), pp.1-7.
https://search.emarefa.net/detail/BIM-1057774
American Medical Association (AMA)
Ming-jun, Deng& Shi-ru, Qu. Fuzzy State Transition and Kalman Filter Applied in Short-Term Traffic Flow Forecasting. Computational Intelligence and Neuroscience. 2015. Vol. 2015, no. 2015, pp.1-7.
https://search.emarefa.net/detail/BIM-1057774
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1057774