Revolutionizing Urban Traffic Management: An AI and Multi-Agent System Approach Leveraging IoT Technology
Abstract
Our study addresses the challenging issue of traffic congestion in modern urban areas and the limitations of traditional solutions like road expansion and network indicators. To effectively tackle traffic congestion, the study explores various strategies that analyze traffic elements, falling into the Macroscopic and Microscopic Models. However, conventional traffic modeling faces significant challenges in dealing with complex traffic systems.
Artificial Intelligence (AI) techniques, including fuzzy logic, evolutionary algorithms, neural networks, and reinforcement learning, emerge as promising solutions for traffic control. For instance, artificial neural networks accurately predict urban traffic flow, and meta-heuristic AI approaches like the artificial bee colony algorithm optimize signal timings.
The rise of the Internet of Things (IoT) offers opportunities for effective traffic analysis by collecting vast and uncertain data. Nevertheless, most existing systems focus on local events, leaving a gap in their effectiveness. To address this, multi-agent systems become a novel strategy, deploying distributed agents across road intersections for comprehensive traffic management.
The paper proposes applying the ant colony optimization algorithm within a distributed multi-agent architecture, leveraging IoT technology, to address path routing challenges in urban traffic. The study emphasizes the potential of advanced AI techniques and multi-agent systems in revolutionizing traffic management for efficient and sustainable urban transportation systems.
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