Autonomous Vehicles and the Future of Urban Mobility: AI-Driven Traffic Optimization
Abstract
Autonomous vehicles (AVs) are reshaping urban transportation, but optimizing their integration into existing traffic systems remains a challenge. This paper presents an AI-driven traffic optimization framework that employs reinforcement learning and multi-agent systems to enhance AV coordination. Our simulations demonstrate improvements in traffic flow efficiency, fuel consumption reduction, and accident minimization. By analyzing real-world traffic data and AV sensor inputs, we propose a scalable approach to smart mobility management. This study provides insights into the future of intelligent transportation systems and their impact on urban infrastructure.
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