The prediction of the traffic behavior could help to make decision about the routing process, as well as enables gains on effectiveness and productivity on the physical distribution. This need motivated the search for technological improvements in the Routing performance in metropolitan areas. The purpose of this paper is to present computational evidences that Artificial Neural Network ANN could be use to predict the traffic behavior in a metropolitan area such So Paulo (around 16 million inhabitants). The proposed methodology involves the application of Rough-Fuzzy Sets to define inference morphology for insertion of the behavior of Dynamic Routing into a structured rule basis, without human expert aid. The dynamics of the traffic parameters are described through membership functions. Rough Sets Theory identifies the attributes that are important, and suggest Fuzzy relations to be inserted on a Rough Neuro Fuzzy Network (RNFN) type Multilayer Perceptron (MLP) and type Radial Basis Function (RBF), in order to get an optimal surface response. To measure the performance of the proposed RNFN, the responses of the unreduced rule basis are compared with the reduced rule one. The results show that by making use of the Feature Reduction through RNFN, it is possible to reduce the need for human expert in the construction of the Fuzzy inference mechanism in such flow process like traffic breakdown. © 2011 IEEE.
Traffic flow breakdown prediction using feature reduction through Rough-Neuro fuzzy Networks
2011-10-24
Conference paper
Electronic Resource
English
Artificial Neural Network , Fuzzy networks , Membership functions , Rough Sets Theory , Fuzzy Sets , Rough Sets , Human expert , Fuzzy inference , Neural networks , Routing performance , Rough set theory , Flow process , Metropolitan area , Dynamic routing , Radial basis function networks , Technological improvements , Fuzzy set theory , Neuro-fuzzy network , Traffic control , Traffic flow breakdown , Traffic parameters , Traffic Breakdown , Rough set , Fuzzy inference mechanism , Radial basis functions , Forecasting , Surface response , Dynamics , Feature Reduction , Fuzzy relations , Multi layer perceptron , Routing process , Traffic behavior , Rule basis
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