Journal of Intelligent Learning Systems and Applications

Volume 10, Issue 2 (May 2018)

ISSN Print: 2150-8402   ISSN Online: 2150-8410

Google-based Impact Factor: 4.31  Citations  h5-index & Ranking

Fuzzy Rules to Improve Traffic Light Decisions in Urban Roads

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DOI: 10.4236/jilsa.2018.102003    618 Downloads   1,109 Views  


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 However, the traditional semaphore operative ways are outdated.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.

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A. Castán Rocha, J. , Ibarra Martínez, S. , Laria Menchaca, J. , D. Terán Villanueva, J. , G. Treviño Berrones, M. , Pérez Cobos, J. and Uribe Agundis, D. (2018) Fuzzy Rules to Improve Traffic Light Decisions in Urban Roads. Journal of Intelligent Learning Systems and Applications, 10, 36-45. doi: 10.4236/jilsa.2018.102003.

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