By Topic

A Machine Learning Method for Dynamic Traffic Control and Guidance on Freeway Networks

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

3 Author(s)
Kaige Wen ; Coll. of Autom., Northwestern Polytech. Univ., Xi'an ; Shiru Qu ; Yumei Zhang

A distributed approach to reinforcement learning in tasks of ramp metering and dynamic route guidance is presented. The problem domain, a freeway integration control application, is formulated as a distributed reinforcement learning problem. The DRL approach was implemented via a multi-agent control architecture where the decision agent was assigned to each of the on-ramp or VMS. The return of each agent is simultaneously updating a single shared policy. The control strategypsilas efficiency is demonstrated through its application to the simple freeway network. Analyses of simulation results using this approach show significant improvement over traditional local control, especially for the case of large traffic demand. Using the DRL approach, the TTS of the Network has been reduced by 20% under the heavy demands.

Published in:

2009 International Asia Conference on Informatics in Control, Automation and Robotics

Date of Conference:

1-2 Feb. 2009