A Proposal For Modeling Intersections In Traffic Systems By Using Adaptive Fuzzy Petri Nets

It can be observed that the number of vehicles, accidents and congestion increase proportionally with the development of a city. A vehicular traffic system behaves like a discrete event system, and due to variations that affect the level of congestion, modeling and controlling this system becomes a complex task. Petri Nets are one of the most powerful tools for modeling graphically and mathematically. Some systems are characterized by little information, inaccurate data and / or permanent changes with regard to the model of the system, which makes modeling and control difficult. This has led to modeling techniques that apply adaptation techniques and human knowledge representation through bio-inspired computing systems such as Neural Networks and Fuzzy Logic. These techniques will be harnessed in this work in terms of an approximated model for learning in a discrete concurrent system by using Fuzzy Petri Nets to represent knowledge through the application of inference and adaptive rules in a chaotic environment, like it can be found in a traffic system.