If we live in a metropolitan city, traffic is an immense problem of our day to day life. The fundamental explanations for this is, increase of individual vehicles, poor street infrastructure, absence of appropriate road and old ordinary methods for managing traffic. We need to spend a lot of time in rush hour gridlock. Also this leads to a lot of fuel combustion which is a major cause of pollution and health hazards. We have seen that even though amount of vehicles along a particular traffic signal is less, it runs according to a particular allotted time. By using this algorithm we will predict the exact optimal time required by a traffic signal to be made green based on the amount of vehicles present in its lane. In this paper we are trying to minimize traffic clog issue with the assistance of distributive traffic control, using object identification strategy (YOLO) for vehicle counting. The productivity of our proposed framework lies in the fact that this system (DTCS) manages traffic signals depending on the present circumstance of vehicular volume present in its lane and not on pre-assigned time. We have compared two different techniques for counting vehicles which is edge detection and the current state of the art YOLO algorithm.

Traffice Congestion, Distributed traffice control, edge detection, You only Look Once (Yolo)

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