The cities are not static environments. They change constantly. When we talk about traffic in\r\nthe city, the evolution of traffic lights is a journey from mindless automation to increasingly\r\nintelligent, fluid traffic management. In our approach, presented in this paper, reinforcementlearning\r\nmechanism based on cost function is introduced to determine optimal decisions for each\r\ntraffic light, based on the solution given by Larry Page for page ranking in Web environment\r\nPage et al. 1999. Our approach is similar with work presented by Sheng-Chung et al. 2009\r\nand Yousef et al. 2010. We consider that the traffic lights are controlled by servers and a score\r\nfor each road is computed based on efficient PageRank approach and is used in cost function to\r\ndetermine optimal decisions. We demonstrate that the cumulative contribution of each car in the\r\ntraffic respects the main constrain of PageRank approach, preserving all the properties ofMmatrix\r\nconsider in our model.
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