Traffic network micro-simulation model and control algorithm based on approximate dynamic programming

This study presents the adaptive traffic signal control algorithm in a distributed traffic network system. The proposed algorithm is based on a micro-simulation model and a reinforcement learning method, namely approximate dynamic programming (ADP). By considering traffic environment in discrete tim...

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Veröffentlicht in:	IET intelligent transport systems Jg. 10; H. 3; S. 186 - 196
Hauptverfasser:	Yin, Biao, Dridi, Mahjoub, El Moudni, Abdellah
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	The Institution of Engineering and Technology 01.04.2016 Institution of Engineering and Technology
Schlagworte:	adaptive control adaptive traffic signal control algorithm ADP method Algorithms approximate dynamic programming cellular automata cellular automata theory Computer Science Control theory distributed traffic network system Dynamic programming intelligent transportation systems Intersections learning (artificial intelligence) Mathematics microscopic traffic dynamic model Modeling and Simulation Networks reinforcement learning method Traffic engineering Traffic flow traffic network loading environment traffic network microsimulation model Traffic signals vehicle‐following model approximate dynamic programming vehicle-following model microscopic traffic dynamic model dynamic programming intelligent transportation systems adaptive control ADP method distributed traffic network system cellular automata cellular automata theory learning (artificial intelligence) reinforcement learning method traffic network loading environment traffic network microsimulation model adaptive traffic signal control algorithm
ISSN:	1751-956X, 1751-9578
Online-Zugang:	Volltext
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