Scipediacontent: Scipediacontent moved page Draft Content 243984666 to Li et al 2020h

2021-02-12T11:38:38Z

Scipediacontent moved page Draft Content 243984666 to Li et al 2020h

Scipediacontent: Created page with " == Abstract == Controlling traffic signals to alleviate increasing traffic pressure is a concept that has received public attention for a long time. However, existing system..."

2021-02-12T11:38:33Z

Created page with " == Abstract == Controlling traffic signals to alleviate increasing traffic pressure is a concept that has received public attention for a long time. However, existing system..."

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== Abstract ==

Controlling traffic signals to alleviate increasing traffic pressure is a concept that has received public attention for a long time. However, existing systems and methodologies for controlling traffic signals are insufficient for addressing the problem. To this end, we build a truly adaptive traffic signal control model in a traffic microsimulator, i.e., “Simulation of Urban Mobility” (SUMO), using the technology of modern deep reinforcement learning. The model is proposed based on a deep Q-network algorithm that precisely represents the elements associated with the problem: agents, environments, and actions. The real-time state of traffic, including the number of vehicles and the average speed, at one or more intersections is used as an input to the model. To reduce the average waiting time, the agents provide an optimal traffic signal phase and duration that should be implemented in both single-intersection cases and multi-intersection cases. The co-operation between agents enables the model to achieve an improvement in overall performance in a large road network. By testing with data sets pertaining to three different traffic conditions, we prove that the proposed model is better than other methods (e.g., Q-learning method, longest queue first method, and Webster fixed timing control method) for all cases. The proposed model reduces both the average waiting time and travel time, and it becomes more advantageous as the traffic environment becomes more complex.

Document type: Article

== Full document ==
<pdf>Media:Draft_Content_243984666-beopen33-6706-document.pdf</pdf>

== Original document ==

The different versions of the original document can be found in:

* [http://dx.doi.org/10.1155/2020/6505893 http://dx.doi.org/10.1155/2020/6505893] under the license http://creativecommons.org/licenses/by/4.0/

* [https://doi.org/10.1155/2020/6505893 https://doi.org/10.1155/2020/6505893]

* [http://downloads.hindawi.com/journals/jat/2020/6505893.pdf http://downloads.hindawi.com/journals/jat/2020/6505893.pdf],
: [http://downloads.hindawi.com/journals/jat/2020/6505893.xml http://downloads.hindawi.com/journals/jat/2020/6505893.xml],
: [http://dx.doi.org/10.1155/2020/6505893 http://dx.doi.org/10.1155/2020/6505893] under the license cc-by

* [http://downloads.hindawi.com/journals/jat/2020/6505893.pdf http://downloads.hindawi.com/journals/jat/2020/6505893.pdf],
: [https://www.hindawi.com/journals/jat/2020/6505893 https://www.hindawi.com/journals/jat/2020/6505893],
: [https://academic.microsoft.com/#/detail/3046369858 https://academic.microsoft.com/#/detail/3046369858] under the license http://creativecommons.org/licenses/by/4.0/

* [http://dx.doi.org/10.1155/2020/6505893 http://dx.doi.org/10.1155/2020/6505893],
: [https://doaj.org/toc/0197-6729 https://doaj.org/toc/0197-6729],
: [https://doaj.org/toc/2042-3195 https://doaj.org/toc/2042-3195]

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Li et al 2020h - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 243984666 to Li et al 2020h

Scipediacontent: Created page with " == Abstract == Controlling traffic signals to alleviate increasing traffic pressure is a concept that has received public attention for a long time. However, existing system..."