Abstract
AI-powered transportation systems revolutionize urban mobility by dynamically adjusting traffic flow, reducing fuel consumption, emissions, and accidents. Real-time data processing identifies risks, prioritizes emergency vehicles, and optimizes commute times and energy use. Machine learning algorithms preemptively address bottlenecks, refine traffic dynamics, and enhance economic productivity. These systems facilitate seamless vehicle-infrastructure communication, offering personalized route recommendations and optimized traffic signal timings. Ultimately, they create a smart, interconnected urban ecosystem prioritizing efficiency, safety, and sustainability, transforming transportation management.
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