Traffic flow breakdown prediction using feature reduction through Rough-Neuro fuzzy Networks

dc.contributorUniversidade Estadual Paulista (UNESP)
dc.creatorAffonso, C.
dc.creatorSassi, R. J.
dc.creatorFerreira, R. P.
dc.date2014-05-27T11:26:06Z
dc.date2016-10-25T18:34:56Z
dc.date2014-05-27T11:26:06Z
dc.date2016-10-25T18:34:56Z
dc.date2011-10-24
dc.date.accessioned2017-04-06T01:53:14Z
dc.date.available2017-04-06T01:53:14Z
dc.identifierProceedings of the International Joint Conference on Neural Networks, p. 1943-1947.
dc.identifierhttp://hdl.handle.net/11449/72753
dc.identifierhttp://acervodigital.unesp.br/handle/11449/72753
dc.identifier10.1109/IJCNN.2011.6033462
dc.identifierWOS:000297541202011
dc.identifier2-s2.0-80054737095
dc.identifierhttp://dx.doi.org/10.1109/IJCNN.2011.6033462
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/893595
dc.descriptionThe 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.
dc.languageeng
dc.relationProceedings of the International Joint Conference on Neural Networks
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectArtificial Neural Network
dc.subjectFeature Reduction
dc.subjectFuzzy Sets
dc.subjectRough Sets
dc.subjectTraffic Breakdown
dc.subjectDynamic routing
dc.subjectFeature reduction
dc.subjectFlow process
dc.subjectFuzzy inference mechanism
dc.subjectFuzzy networks
dc.subjectFuzzy relations
dc.subjectHuman expert
dc.subjectMetropolitan area
dc.subjectMulti layer perceptron
dc.subjectNeuro-fuzzy network
dc.subjectRadial basis functions
dc.subjectRough set
dc.subjectRough Sets Theory
dc.subjectRouting performance
dc.subjectRouting process
dc.subjectRule basis
dc.subjectSurface response
dc.subjectTechnological improvements
dc.subjectTraffic behavior
dc.subjectTraffic flow breakdown
dc.subjectTraffic parameters
dc.subjectDynamics
dc.subjectForecasting
dc.subjectFuzzy inference
dc.subjectFuzzy set theory
dc.subjectFuzzy sets
dc.subjectMembership functions
dc.subjectRadial basis function networks
dc.subjectRough set theory
dc.subjectTraffic control
dc.subjectNeural networks
dc.titleTraffic flow breakdown prediction using feature reduction through Rough-Neuro fuzzy Networks
dc.typeOtro


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