Scipediacontent: Scipediacontent moved page Draft Content 120056455 to Huang et al 2019b

2021-02-15T08:42:38Z

Scipediacontent moved page Draft Content 120056455 to Huang et al 2019b

Scipediacontent: Created page with " == Abstract == Reinforcement learning (RL)-based traffic signal control has been proven to have great potential in alleviating traffic congestion. The state definition, whic..."

2021-02-15T08:42:35Z

Created page with " == Abstract == Reinforcement learning (RL)-based traffic signal control has been proven to have great potential in alleviating traffic congestion. The state definition, whic..."

New page

== Abstract ==

Reinforcement learning (RL)-based traffic signal control has been proven to have great potential in alleviating traffic congestion. The state definition, which is a key element in RL-based traffic signal control, plays a vital role. However, the data used for state definition in the literature are either coarse or difficult to measure directly using the prevailing detection systems for signal control. This paper proposes a deep reinforcement learning-based traffic signal control method which uses high-resolution event-based data, aiming to achieve cost-effective and efficient adaptive traffic signal control. High-resolution event-based data, which records the time when each vehicle-detector actuation/de-actuation event occurs, is informative and can be collected directly from vehicle-actuated detectors (e.g., inductive loops) with current technologies. Given the event-based data, deep learning techniques are employed to automatically extract useful features for traffic signal control. The proposed method is benchmarked with two commonly used traffic signal control strategies, i.e., the fixed-time control strategy and the actuated control strategy, and experimental results reveal that the proposed method significantly outperforms the commonly used control strategies.

Document type: Article

== Full document ==
<pdf>Media:Draft_Content_120056455-beopen583-1898-document.pdf</pdf>

== Original document ==

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

* [http://dx.doi.org/10.3390/e21080744 http://dx.doi.org/10.3390/e21080744] under the license https://creativecommons.org/licenses/by

* [https://www.mdpi.com/1099-4300/21/8/744/pdf https://www.mdpi.com/1099-4300/21/8/744/pdf] under the license http://creativecommons.org/licenses/by/3.0/

* [https://dblp.uni-trier.de/db/journals/entropy/entropy21.html#WangXHZC19 https://dblp.uni-trier.de/db/journals/entropy/entropy21.html#WangXHZC19],
: [https://www.mdpi.com/1099-4300/21/8/744 https://www.mdpi.com/1099-4300/21/8/744],
: [https://www.mdpi.com/1099-4300/21/8/744/pdf https://www.mdpi.com/1099-4300/21/8/744/pdf],
: [https://doi.org/10.3390/e21080744 https://doi.org/10.3390/e21080744],
: [https://academic.microsoft.com/#/detail/2965341638 https://academic.microsoft.com/#/detail/2965341638] under the license cc-by

* [https://www.mdpi.com/1099-4300/21/8/744 https://www.mdpi.com/1099-4300/21/8/744],
: [https://doaj.org/toc/1099-4300 https://doaj.org/toc/1099-4300]

* [https://www.mdpi.com/1099-4300/21/8/744/pdf https://www.mdpi.com/1099-4300/21/8/744/pdf],
: [http://dx.doi.org/10.3390/e21080744 http://dx.doi.org/10.3390/e21080744]

under the license https://creativecommons.org/licenses/by/4.0/

← Older revision	Revision as of 08:42, 15 February 2021
(No difference)

Huang et al 2019b - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 120056455 to Huang et al 2019b

Scipediacontent: Created page with " == Abstract == Reinforcement learning (RL)-based traffic signal control has been proven to have great potential in alleviating traffic congestion. The state definition, whic..."