AI for Sustainable Urban Mobility: Optimizing Public Transportation to Reduce Emissions
Abstract
Public transportation plays a crucial role in reducing urban traffic congestion and emissions, yet optimizing these systems for sustainability remains challenging. This study examines the use of AI-driven algorithms to enhance urban mobility by optimizing public transit schedules, routes, and load balancing. By analyzing passenger flow, traffic patterns, and environmental data, the AI model adjusts transit operations to maximize efficiency and reduce emissions. The findings highlight significant reductions in fuel use and greenhouse gas emissions, demonstrating how AI can support sustainable transportation and contribute to cleaner urban environments.
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References
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