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DC Field | Value | Language |
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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 |
Appears in Collections: | Master |
Files in This Item:
File | Description | Size | Format | |
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BEN TADJINE_AMEL_F5.pdf | 1,32 MB | Adobe PDF | View/Open |
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