By Topic

Adaptive Fuzzy Urban Traffic Flow Control Using a Cooperative Multi-Agent System based on Two Stage Fuzzy Clustering

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Fatemeh Daneshfar ; Dept. of Electr. & Comput. Eng., Univ. of Kurdistan, Sanandaj ; Javad RavanJamJah ; Fathollah Mansoori ; Hassan Bevrani
more authors

The traffic congestion problem in urban areas is worsening since traditional traffic signal control systems cannot provide efficient traffic control. Therefore, dynamic traffic signal control in Intelligent Transportation System (ITS) recently has received increasing attention. This study devises an adaptive and cooperative multi-agent fuzzy system for a decentralized traffic signal control. To achieve this we have worked on a model which has three levels of control. Every intersection is controlled by its own traffic situation, correlated intersections recommendations and a knowledge base which provides its traffic pattern. This study focused on utilizing the prediction mechanism of our architecture, it finds most correlated intersections based on a two stage fuzzy clustering algorithm which finds most intersections effect on a specific intersection based on clustering membership degree. We have also developed a NetLogo-based traffic simulator to serve as the agents' world. Our approach is tested with traffic control of a large connected junctions and the result obtained is promising: The average delay time can be reduced by 42.76% compared to the conventional fixed sequence traffic signal and 28.77% compared to the vehicle actuated traffic control strategy.

Published in:

Vehicular Technology Conference, 2009. VTC Spring 2009. IEEE 69th

Date of Conference:

26-29 April 2009