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
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Beschreibung
Zusammenfassung: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 time, the microscopic traffic dynamic model is built. In particular, the authors explore a vehicle-following model using cellular automata theory. This vehicle-following model theoretically contributes to traffic network loading environment in an accessible way. To make the network coordinated, tunable state with weights of queue length and vehicles on lane is considered. The intersection can share information with each other in this state representation and make a joint action for intersection coordination. Moreover, the traffic signal control algorithm based on ADP method performs quite well in different performance measures witnessed by simulations. By comparing with other control methods, experimental results present that the proposed algorithm could be a potential candidate in an application of traffic network control system.
Bibliographie:ObjectType-Article-1
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ISSN:1751-956X
1751-9578
DOI:10.1049/iet-its.2015.0108