Scipediacontent: Scipediacontent moved page Draft Content 711024922 to Wiering et al 2017a

2021-02-16T10:22:56Z

Scipediacontent moved page Draft Content 711024922 to Wiering et al 2017a

Scipediacontent: Created page with " == Abstract == The transportation demand is rapidly growing in metropolises, resulting in chronic traffic congestions in dense downtown areas. Adaptive traffic signal con..."

2021-02-16T10:22:53Z

Created page with " == Abstract == The transportation demand is rapidly growing in metropolises, resulting in chronic traffic congestions in dense downtown areas. Adaptive traffic signal con..."

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== Abstract ==

The transportation demand is rapidly growing in metropolises, resulting in chronic traffic congestions in dense downtown areas. Adaptive traffic signal control as the principle part of intelligent transportation systems has a primary role to effectively reduce traffic congestion by making a real-time adaptation in response to the changing traffic network dynamics. Reinforcement learning (RL) is an effective approach in machine learning that has been applied for designing adaptive traffic signal controllers. One of the most efficient and robust type of RL algorithms are continuous state actor-critic algorithms that have the advantage of fast learning and the ability to generalize to new and unseen traffic conditions. These algorithms are utilized in this paper to design adaptive traffic signal controllers called actor-critic adaptive traffic signal controllers (A-CATs controllers). The contribution of the present work rests on the integration of three threads: (a) showing performance comparisons of both discrete and continuous A-CATs controllers in a traffic network with recurring congestion (24-h traffic demand) in the upper downtown core of Tehran city, (b) analyzing the effects of different traffic disruptions including opportunistic pedestrians crossing, parking lane, non-recurring congestion, and different levels of sensor noise on the performance of A-CATS controllers, and (c) comparing the performance of different function approximators (tile coding and radial basis function) on the learning of A-CATs controllers. To this end, first an agent-based traffic simulation of the study area is carried out. Then six different scenarios are conducted to find the best A-CATs controller that is robust enough against different traffic disruptions. We observe that the A-CATs controller based on radial basis function networks (RBF (5)) outperforms others. This controller is benchmarked against controllers of discrete state Q-learning, Bayesian Q-learning, fixed time and actuated controllers; and the results reveal that it consistently outperforms them.

== Original document ==

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

* [http://dx.doi.org/10.1016/j.trc.2017.09.020 http://dx.doi.org/10.1016/j.trc.2017.09.020]

* [https://pure.rug.nl/ws/files/50470700/Paper_Final.pdf https://pure.rug.nl/ws/files/50470700/Paper_Final.pdf]

* [https://www.rug.nl/research/portal/en/publications/adaptive-traffic-signal-control-with-actorcritic-methods-in-a-realworld-traffic-network-with-different-traffic-disruption-events(7662f678-fa9a-47c5-92ba-1fc1c4639d1e).html https://www.rug.nl/research/portal/en/publications/adaptive-traffic-signal-control-with-actorcritic-methods-in-a-realworld-traffic-network-with-different-traffic-disruption-events(7662f678-fa9a-47c5-92ba-1fc1c4639d1e).html]

* [https://api.elsevier.com/content/article/PII:S0968090X17302681?httpAccept=text/xml https://api.elsevier.com/content/article/PII:S0968090X17302681?httpAccept=text/xml],
: [https://api.elsevier.com/content/article/PII:S0968090X17302681?httpAccept=text/plain https://api.elsevier.com/content/article/PII:S0968090X17302681?httpAccept=text/plain],
: [http://dx.doi.org/10.1016/j.trc.2017.09.020 http://dx.doi.org/10.1016/j.trc.2017.09.020] under the license https://www.elsevier.com/tdm/userlicense/1.0/

* [https://www.sciencedirect.com/science/article/pii/S0968090X17302681 https://www.sciencedirect.com/science/article/pii/S0968090X17302681],
: [https://www.rug.nl/research/portal/files/50470700/Paper_Final.pdf https://www.rug.nl/research/portal/files/50470700/Paper_Final.pdf],
: [https://www.rug.nl/research/portal/publications/adaptive-traffic-signal-control-with-actorcritic-methods-in-a-realworld-traffic-network-with-different-traffic-disruption-events(7662f678-fa9a-47c5-92ba-1fc1c4639d1e).html https://www.rug.nl/research/portal/publications/adaptive-traffic-signal-control-with-actorcritic-methods-in-a-realworld-traffic-network-with-different-traffic-disruption-events(7662f678-fa9a-47c5-92ba-1fc1c4639d1e).html],
: [https://trid.trb.org/view/1492148 https://trid.trb.org/view/1492148],
: [https://pure.rug.nl/ws/files/50470700/Paper_Final.pdf https://pure.rug.nl/ws/files/50470700/Paper_Final.pdf],
: [https://core.ac.uk/display/148336316 https://core.ac.uk/display/148336316],
: [https://academic.microsoft.com/#/detail/2766381093 https://academic.microsoft.com/#/detail/2766381093]

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Scipediacontent: Scipediacontent moved page Draft Content 711024922 to Wiering et al 2017a

Scipediacontent: Created page with " == Abstract == The transportation demand is rapidly growing in metropolises, resulting in chronic traffic congestions in dense downtown areas. Adaptive traffic signal con..."