TITLE:
Fuzzy Rules to Improve Traffic Light Decisions in Urban Roads
AUTHORS:
J. A. Castán Rocha, S. Ibarra Martínez, J. Laria Menchaca, J. D. Terán Villanueva, M. G. Treviño Berrones, J. Pérez Cobos, D. Uribe Agundis
KEYWORDS:
Fuzzy Inference System, Urban Traffic Control, Vehicular Mobility, Intelligent Transport System
JOURNAL NAME:
Journal of Intelligent Learning Systems and Applications,
Vol.10 No.2,
May
22,
2018
ABSTRACT: Many researchers around the world are looking for developing techniques or technologies that cover traditional and recent constraints in urban traffic con-trol. Normally, such traffic devices are facing with a large scale of input data when they must to response in a reliable, suitable and fast way. Because of such statement, the paper is devoted to introduce a proposal for enhancing the traffic light decisions. The principal goal is that a semaphore can provide a correct and fluent vehicular mobility. However, the traditional semaphore operative ways are outdated. We present in a previous contribution the development of a methodology capable of improving the vehicular mobility by proposing a new green light interval based on road conditions with a CBR approach. However, this proposal should include whether it is needed to modify such light duration. To do this, the paper proposes the adaptation of a fuzzy inference system helping to decide when the semaphore should try to fix the green light interval according to specific road requirements. Some experiments are conducted in a simulated environment to evaluate the pertinence of implementing a decision-making before the CBR methodology. For example, using a fuzzy inference approach the decisions of the system improve almost 18% in a set of 10,000 experiments. Finally, some conclusions are drawn to emphasize the benefits of including this technique in a methodology to implement intelligent semaphores.