DocumentCode
2669400
Title
Predicting traffic flow changes by fuzzy logic
Author
Kamenev, A.V. ; Pashchenko, F.F. ; Kudinov, Y.I.
Author_Institution
Inst. of Control Sci. V.A. Trapeznikov, Moscow, Russia
fYear
2012
fDate
23-25 May 2012
Firstpage
1570
Lastpage
1572
Abstract
Fuzzy models with hybrid algorithms of adaptation allow to predict complex temporary processes successfully. This article shows how we can use fuzzy difference TSK model for traffic flow predictions. Described hybrid algorithm, it uses fuzzy logic, neuro - fuzzy network and recurrent algorithm. Hybrid algorithm consists of two types identifications: parametric and structural. We apply it for traffic data from California and compare with another prediction methods.
Keywords
fuzzy neural nets; fuzzy set theory; road traffic; fuzzy difference TSK model; fuzzy logic; neuro-fuzzy network; recurrent algorithm; traffic flow predictions; Adaptation models; Algorithm design and analysis; Mathematical model; Prediction algorithms; Predictive models; Roads; Vectors; fuzzy model; hybrid algorithm; model; parameter estimation; system; the rule of inference; traffic flow;
fLanguage
English
Publisher
ieee
Conference_Titel
Control and Decision Conference (CCDC), 2012 24th Chinese
Conference_Location
Taiyuan
Print_ISBN
978-1-4577-2073-4
Type
conf
DOI
10.1109/CCDC.2012.6244255
Filename
6244255
Link To Document