title: Fuzzy State Transition and Kalman Filter Applied in Short-Term Traffic Flow Forecasting.
abstract: 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.
authors.affiliation: School of Automatic Control, Northwestern Polytechnical University, Xian 710072, China; School of Civil Architecture, East China Jiaotong University, Nanchang 330013, China.
authors.forename: Ming-jun
authors.initials: MJ
authors.lastname: Deng
authors.affiliation: School of Automatic Control, Northwestern Polytechnical University, Xian 710072, China.
authors.forename: Shi-ru
authors.initials: SR
authors.lastname: Qu
mesh_terms: D000465:Algorithms; D001336:Automobiles; D001499:Bayes Theorem; D002681:China; D005544:Forecasting; D017143:Fuzzy Logic; D015233:Models, Statistical