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
computatio
