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This content will become publicly available on January 31, 2016

Title: A junction-tree based learning algorithm to optimize network wide traffic control: A coordinated multi-agent framework

Our study develops a novel reinforcement learning algorithm for the challenging coordinated signal control problem. Traffic signals are modeled as intelligent agents interacting with the stochastic traffic environment. The model is built on the framework of coordinated reinforcement learning. The Junction Tree Algorithm (JTA) based reinforcement learning is proposed to obtain an exact inference of the best joint actions for all the coordinated intersections. Moreover, the algorithm is implemented and tested with a network containing 18 signalized intersections in VISSIM. Finally, our results show that the JTA based algorithm outperforms independent learning (Q-learning), real-time adaptive learning, and fixed timing plans in terms of average delay, number of stops, and vehicular emissions at the network level.
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  1. Purdue Univ., West Lafayette, IN (United States)
Publication Date:
OSTI Identifier:
Grant/Contract Number:
AC05-00OR22725; 1004528; 104IPY04
Accepted Manuscript
Journal Name:
Transportation Research Part C: Emerging Technologies
Additional Journal Information:
Journal Volume: 58; Journal Issue: PC; Journal ID: ISSN 0968-090X
Research Org:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org:
USDOE, National Science Foundation (NSF)
Country of Publication:
United States
97 MATHEMATICS AND COMPUTING; Traffic Control; Learning Algorithm; Multi-agent control; Connected Vehicle