A Methodology for the Automatic Regulation of Intersections in Real Time Using Soft-Computing Techniques
This work presents an application of diverse soft-computing techniques to the resolution of semaphoric regulation problems. First, clustering techniques are used to discover the prototypes which characterize the mobility patterns at an intersection. A prediction model is then constructed on the basis of the prototypes found. Fuzzy logic techniques are used to formally represent the prototypes in this prediction model and these prototypes are parametrically defined through frameworks. The use of these techniques supposes a substancial contribution to the significance of the prediction model, making it robust in the face of anomalous mobility patterns, and efficient from the point of view of real-time computation.
KeywordsRegulating traffic lights soft-computing clustering estimation models
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