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dc.contributor.author |
BEN TADJINE, AMEL |
|
dc.date.accessioned |
2022-10-16T09:27:56Z |
|
dc.date.available |
2022-10-16T09:27:56Z |
|
dc.date.issued |
2022 |
|
dc.identifier.uri |
http://dspace.univ-guelma.dz/jspui/handle/123456789/13285 |
|
dc.description.abstract |
Nowadays, transport has become an essential element for the modern societies. So the
management of networks has become also important. Among the most used tools for the
management of these networks, we find traffic lights. These lights do not adapt to the amount
of traffic (fixed time for each traffic light). The evolution of new technologies has made it
possible to solve this problem and to make traffic lights smart.
The objective of this work is to propose a new dynamic control solution l for intelligent
traffic lights using reinforcement learning combined with deep learning. The main advantage
of our system is to provide adaptation between traffic lights and smooth traffic flow in
different conditions. |
en_US |
dc.language.iso |
fr |
en_US |
dc.publisher |
université de guelma |
en_US |
dc.subject |
Signalisation intelligente, Contrôle de trafic, Apprentissage par renforcement, Apprentissage profond. |
en_US |
dc.title |
Conception d’un modèle de contrôle adaptatif du trafic |
en_US |
dc.type |
Working Paper |
en_US |
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