<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></abstract>

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.

Ramp Metering Control Based on the Q-Learning Algorithm

Faculty of Transport and Traffic Sciences, University of Zagreb, Vukelićeva 4, HR-10000 Zagreb, Republic of Croatia

Faculty of Technical Sciences, Bitola, St. Kliment Ohridski University, Makedonska falanga 33, 7000 Bitola, Republic of Macedonia

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"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...

Ramp Metering Control Based on the Q-Learning Algorithm

Pubblicato online: 30 apr 2015

Pagine: 88 - 97

DOI: https://doi.org/10.1515/cait-2015-0019

© by Edouard Ivanjko

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.