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Mathematical Problems in Engineering
Volume 2012, Article ID 465613, 9 pages
Research Article

An Efficient PageRank Approach for Urban Traffic Optimization

Faculty of Automatic Control and Computers, University Politehnica of Bucharest, Splaiul Independentei 313, 060042 Bucharest, Romania

Received 24 April 2012; Accepted 9 June 2012

Academic Editor: Ming Li

Copyright © 2012 Florin Pop and Ciprian Dobre. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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