Ramp Metering Control Based on the Q-Learning Algorithm

Modern urban highways are under the influence of increased traffic demand and cannot fulfill the desired level of service anymore. In most of the cases there is no space available for any infrastructure building. Solutions from the domain of intelligent transport systems are used, such as ramp meter...

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Bibliographic Details
Published in:Cybernetics and information technologies : CIT 2015-01, Vol.15 (5), p.88-97
Main Authors: Ivanjko, Edouard, Koltovska Nečoska, Daniela, Gregurić, Martin, Vujić, Miroslav, Jurković, Goran, Mandžuka, Sadko
Format: Article
Language:English
Subjects:
Algorithms
artificial intelligence
autonomic systems
Computer simulation
Construction
Demand
Metering
microscopic simulation
Q-Learning
Ramp metering
Ramps
Traffic engineering
Traffic flow
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Summary:Modern urban highways are under the influence of increased traffic demand and cannot fulfill the desired level of service anymore. In most of the cases there is no space available for any infrastructure building. Solutions from the domain of intelligent transport systems are used, such as ramp metering. To cope with the significant daily changes of the traffic demand, various approaches with autonomic properties like self-learning are applied for ramp metering. One of these approaches is reinforced learning. In this paper the Q-Learning algorithm is applied to learn the local ramp metering control law in a simulation environment, implemented in a VISSIM microscopic simulator. The approach proposed is tested in simulations with emphasis on the mainstream speed and travel time, using a typical on-ramp configuration.
ISSN:1314-4081
1311-9702
1314-4081
DOI:10.1515/cait-2015-0019