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Enhancing Transportation Systems and Urban Mobility
Abstract
This paper investigates the transformative impact of AI-driven engineering on transportation systems and urban mobility. Through case studies and research insights, it explores how artificial intelligence is revolutionizing various aspects of transportation, including traffic management, public transit, and autonomous vehicles. The study highlights the application of AI techniques such as machine learning, reinforcement learning, and computer vision in optimizing traffic flow, reducing congestion, and enhancing safety on roadways. Additionally, it discusses the integration of AI with emerging technologies such as connected vehicles, smart infrastructure, and ride-sharing platforms to improve accessibility, efficiency, and sustainability of urban transportation networks. The paper also addresses challenges such as cybersecurity, regulatory compliance, and ethical considerations in the adoption of AI-driven engineering solutions in transportation. It emphasizes the need for interdisciplinary collaboration, policy support, and public engagement to leverage AI's potential in creating more efficient, equitable, and sustainable transportation systems for future cities.
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References
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