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Enhancing Traffic Signal Control: A Value Decomposition Approach Empowered by Communication-multiple Agent Approval

Abstract

Omar Salameh

Traffic congestion is a perennial challenge in urban environments, with traffic signal control systems playing a crucial role in managing flow and reducing congestion. Traditional traffic signal control systems often lack adaptability and responsiveness to dynamic traffic conditions. In recent years, advancements in Artificial Intelligence (AI) have paved the way for innovative approaches to traffic signal control. One such approach is the integration of value decomposition techniques with communication-multiple agent approval systems. This article explores the synergy between these methodologies and their potential to revolutionize traffic signal control, leading to more efficient and adaptive traffic management solutions.