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Anfis-Based Intelligent Traffic Control System (ITCS) for Developing Cities

Oluwafemi O. Awoyera, Oumar Sacko, Omar Darboe, and Onyia Chinwe Cynthia
Department of Electrical Engineering Pan African University Institute of Basic Sciences, Technology and Innovation Nairobi, Kenya

Abstract—Intelligent Traffic Control System (ITCS) is needed to effectively control the flow of traffic on major road networks in developing cities. The ITCS system resolves the limitations of traditional traffic control systems which assign fixed green time, and give inefficient control in unforeseen traffic situations. Artificial Neural Network (ANN) techniques can be used to optimize the flow of traffic at a traffic junction, based on real-time information oftraffic volume on different lanes. In this work, we present a novel traffic control scheme- ITCS- with machine learning abilities. The Intelligent Traffic Control System (ITCS) consists of Closed-circuit television (CCTV) cameras that take photograph of each traffic lane in real time, and send to the Image Processing unit which determines the volume of traffic on that particular lane. The ITCS then assigns a priority to each lane based on the current traffic volume on it. The priority weights can be adapted in real time, and are capable of responding to traffic changes caused by unforeseen events. The Adaptive Neuro-Fuzzy Inference System (ANFIS)-based traffic control system can learn from past traffic data and can predict future traffic on a particular road, by observing the traffic on the adjoining roads. The ITCS system will help to alleviate traffic congestions on major city roads and reduce unproductive time, economic stagnation, and green house emissions in smart cities.

Index Terms—intelligent traffic control, ANFIS, real-time traffic, softcomputing, smart cities

Cite: Oluwafemi O. Awoyera, Oumar Sacko, Omar Darboe, and Onyia Chinwe Cynthia, "Anfis-Based Intelligent Traffic Control System (ITCS) for Developing Cities," Journal of Traffic and Logistics Engineering, Vol. 7, No. 1, pp. 18-22, June 2019. doi: 10.18178/jtle.7.1.18-22

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