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
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;
Conference_Titel :
Control and Decision Conference (CCDC), 2012 24th Chinese
Conference_Location :
Taiyuan
Print_ISBN :
978-1-4577-2073-4
DOI :
10.1109/CCDC.2012.6244255
