OPTIMIZATION STRATEGY BASED ON GENETIC FUZZY LOGIC APPLIED IN PUBLIC TRANSPORTATION MANAGEMENT

Resumo

The search for the implementation of intelligent systems that provide greater effectiveness
in urban planning, especially urban mobility, in order to facilitate the development of metropolises,
has grown in recent years. The real world is too complicated to get accurate descriptions, therefore
the approximation or inaccuracy must be introduced to get a reasonable model. In view of this, fuzzy
logic presents itself as a powerful tool, since it is able to deal with systems that present problems for
conventional techniques, mainly the nonlinearity and the lack of precise knowledge about these systems.
Initially it was only approached the quantitative population of a given region and o timetable as input
variables. The objective of cluster analysis is to classify objects according to the similarities between
them and the organization of data in groups. Therefore, the Fuzzy C-Means (FCM) fuzzy clustering
algorithm can be used to work with the initial collected data. The centers of the clusters provided by FCM
correspond to the parameters of the rules of the proposed Takagi-Sugeno model. In order to guarantee
the characteristics of performance, robustness and stability, the use of hybrid systems, it is necessary,
specifically, to include the genetic algorithm to aid and optimize the fuzzy system, having as a function
of suitability the output of the Takagi- Sugeno system. Consequently, the current routes are evaluated
according to their cost of implementation, since they include as many people as possible, in order to
validate or even reformulate existing routes.