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Real-time optimization of traffic lights using artificial intelligence.

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dc.contributor.author LOULHACI, Romaissa
dc.contributor.author BENSOUNA, Bouchra
dc.date.accessioned 2024-10-14T09:57:18Z
dc.date.available 2024-10-14T09:57:18Z
dc.date.issued 2024-09-25
dc.identifier.uri http://hdl.handle.net/STDB_UNAM/580
dc.description.abstract Urban traffic congestion is a major challenge, reducing the quality of life for drivers and commuters. Traditional traffic lights worsen the issue due to their inability to adapt to real-time conditions, causing delays and inefficiencies. This thesis explores the application of artificial intelligence, specifically reinforcement learning, to create a smarter traffic light system that can dynamically adjust to varying traffic conditions. By making optimal decisions in different scenarios, this approach was implemented in a region of Tlemcen, where traffic congestion has been a notable issue. This real-world application demonstrates how the AI-based system can adapt to varying traffic conditions specific to the region, enhancing traffic flow, reducing congestion, and minimizing vehicle wait times at intersections. The use of AI in this context provides a more efficient and responsive solution compared to traditional traffic light systems. en_US
dc.language.iso en en_US
dc.publisher Directeur: Mr ABDELLAOUI Ghouti / Co-Directeur: Mrs. SEBBAGH Hafidha en_US
dc.subject Intelligent transport systems, Artificial intelligence, graph theory, urban mobility. en_US
dc.title Real-time optimization of traffic lights using artificial intelligence. en_US
dc.type Thesis en_US


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