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This book offers an in-depth exploration of the theoretical foundations of road traffic management, presents generalities on road traffic theory, reinforcement learning for road traffic control and proposes several cooperative approaches to intelligently optimize traffic light management in a road network and to improve road traffic quality. Drawing on extensive research and literature in the context of adaptive traffic light control, this book begins by elucidating the fundamental theories underlying road traffic management. It examines the state of the art in both intelligent transportation…mehr

Produktbeschreibung
This book offers an in-depth exploration of the theoretical foundations of road traffic management, presents generalities on road traffic theory, reinforcement learning for road traffic control and proposes several cooperative approaches to intelligently optimize traffic light management in a road network and to improve road traffic quality. Drawing on extensive research and literature in the context of adaptive traffic light control, this book begins by elucidating the fundamental theories underlying road traffic management. It examines the state of the art in both intelligent transportation systems and reinforcement learning methods applied to traffic light control. The book's application-oriented approach ensures that readers can translate theory and methodology into tangible improvements within road networks. It presents several approaches to alleviating urban congestion, from solving the problem for an isolated intersection to solving the problem for a network of adjacent intersections.
Autorenporträt
Dott. Tarek Amine Haddad, docente presso la Scuola Nazionale di Energie Rinnovabili, Ambiente e Sviluppo Sostenibile. Ha conseguito un dottorato di ricerca in sistemi informatici presso la Facoltà di Matematica e Informatica dell'Università di Batna 2.