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Enhancing Urban Transportation Systems for Sustainable Mobility
Abstract
This paper explores how AI-driven engineering is enhancing urban transportation systems for sustainable mobility. Through case studies and research insights, it investigates how artificial intelligence is revolutionizing traditional transportation practices, including traffic management, route optimization, and public transit operations. The study highlights the application of AI techniques such as machine learning, computer vision, and reinforcement learning in improving efficiency, safety, and accessibility in urban transportation. Additionally, it discusses the integration of AI with smart mobility solutions, including ride-sharing platforms, autonomous vehicles, and intelligent transportation systems, to enable seamless connectivity and multimodal transportation options. The paper also addresses challenges such as data privacy, regulatory compliance, and social equity in the deployment of AI-driven engineering solutions in urban transportation. It emphasizes the importance of user-centric design, stakeholder engagement, and policy support to leverage AI's potential in transforming urban mobility and creating sustainable cities for the future.
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References
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