Abstract
Urban transportation is a significant contributor to global carbon emissions, exacerbating climate change and urban air quality issues. This study explores how Artificial Intelligence (AI)-driven transportation systems can mitigate these environmental impacts by optimizing routes, reducing congestion, and enhancing public transportation efficiency. Through a combination of empirical data analysis, cities cases, and simulation models, this research evaluates the effectiveness of AI applications in urban mobility. The findings demonstrate that AI-driven optimizations lead to substantial reductions in carbon emissions, improved energy efficiency, and better utilization of existing infrastructure. Additionally, the study addresses the challenges and barriers to implementing AI solutions in urban settings, providing recommendations for policymakers and urban planners to foster sustainable transportation ecosystems. This research contributes to the growing body of knowledge on sustainable urban development and highlights the pivotal role of AI in achieving environmental sustainability goals.
| Original language | English |
|---|---|
| Title of host publication | Americas Conference on Information Systems, AMCIS 2025 |
| Place of Publication | usa |
| Publisher | Association for Information Systems |
| Pages | 4683-4692 |
| Number of pages | 10 |
| Volume | 7 |
| ISBN (Electronic) | 9798331327743 |
| State | Published - Jan 1 2025 |
| Event | 2025 Americas Conference on Information Systems, AMCIS 2025 - Montreal, Canada Duration: Aug 14 2025 → Aug 16 2025 |
Conference
| Conference | 2025 Americas Conference on Information Systems, AMCIS 2025 |
|---|---|
| Country/Territory | Canada |
| Period | 08/14/25 → 08/16/25 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
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SDG 9 Industry, Innovation, and Infrastructure
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SDG 11 Sustainable Cities and Communities
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SDG 13 Climate Action
Keywords
- AI
- Carbon Footprint
- Public Transportation Efficiency
- Traffic Optimization
- Urban Mobility
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