Chapter
Apr 26, 2012
Reinforcement Learning Control for On-Ramp Metering Based on Traffic Simulation
Authors: Xingju Wang [email protected], Bohang Liu, Xueqin Niu, and Toshihiko Miyagi [email protected]Author Affiliations
Publication: ICCTP 2009: Critical Issues In Transportation Systems Planning, Development, and Management
Abstract
Traffic congestion on highways causes decreased efficiency in road operation and increased energy consumption and environmental pollution. Various measures for traffic control have been considered for easing traffic congestion. In this paper, we propose the use of ramp metering control by introducing the reinforcement learning model in artificial intelligence, which is then combined with a dynamic micro traffic style simulation. Numerical simulation showed that when effective Reinforcement Learning ramp metering control is completed in the on-ramp section, traffic confusion can be prevented in advance.
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Published In
ICCTP 2009: Critical Issues In Transportation Systems Planning, Development, and Management
Pages: 1 - 7
Copyright
© 2009 American Society of Civil Engineers.
History
Published online: Apr 26, 2012
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Authors
Affiliations
School of Traffic and Transportation, Shijiazhuang Railway Institute, P.O. Box 050043, Shijiazhuang, Hebei, China. Email: [email protected] and Traffic Safety and Control Laboratory of Hebei Province, P.O. Box 050043, Shijiazhuang, Hebei, China.E-mail: [email protected]
Bohang Liu
School of Traffic and Transportation, Shijiazhuang Railway Institute, P.O. Box 050043, Shijiazhuang, Hebei, China and Traffic Safety and Control Laboratory of Hebei Province, P.O. Box 050043, Shijiazhuang, Hebei, China
Xueqin Niu
School of Traffic and Transportation, Shijiazhuang Railway Institute, P.O. Box 050043, Shijiazhuang, Hebei, China and Traffic Safety and Control Laboratory of Hebei Province, P.O. Box 050043, Shijiazhuang, Hebei, China
Graduate, School of Information Sciences, Tohoku University, P.O. Box 9808578, Sendai, Japan. E-mail: [email protected]
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