Abstract - Traffic congestion is one of the main difficult issue for citizens in the field of road transport, and traffic congestion prediction is a fundamental issue in the field of Intelligent Transportation Systems (ITSs).
This paper presents a comparative study between a hybrid technique that combines a Genetic Algorithm with a Cross Entropy method to optimize Fuzzy Rule-Based Systems, as well as literature techniques. These techniques are applied to traffic congestion datasets in order to determine their performance in this area. Results show that the hybrid technique improves those results obtained by the techniques of the state of the art. In this way, the performed experimentation shows the competitiveness of the proposal in this area of application.
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