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Urban mobility is a critical contributor to greenhouse gas emissions, accounting for over 30% of urban carbon emissions in the United States in 2021. Addressing this challenge requires a comprehensive and data-driven approach to transform transportation systems into sustainable networks. This paper presents an integrated framework that leverages artificial intelligence (AI), machine learning (ML), and life cycle assessment (LCA) to analyze, model, and optimize urban mobility. The framework consists of four key components: AI-powered analysis and models, synthetic urban mobility data generation, LCA for environmental footprint analysis, and data-driven policy interventions. By combining these elements, the framework not only deciphers complex mobility patterns but also quantifies their environmental impacts, providing actionable insights for policy decisions aimed at reducing carbon emissions and promoting sustainable urban transportation. The implications of this approach extend beyond individual cities, offering a blueprint for global sustainable urban mobility.
Chester MV, Horvath A (2009). Environmental assessment of passenger transportation should include infrastructure and supply chains. Environmental Research Letters, 4: 024008.
Enlund J, Andersson D, Carlsson F (2023). Individual carbon footprint reduction: evidence from pro-environmental users of a carbon calculator. Environmental and Resource Economics, 86: 433–467.
Li W, Wang Q, Liu Y, et al. (2022). A spatiotemporal decay model of human mobility when facing large-scale crises. Proceedings of the National Academy of Sciences of the United States of America, 119: e2203042119
Pappalardo L, Manley E, Sekara V, et al. (2023). Future directions in human mobility science. Nature Computational Science, 3: 588–600.
Simini F, Barlacchi G, Luca M, et al. (2021). A Deep Gravity model for mobility flows generation. Nature Communications, 12: 6576.
Van Mierlo J, Messagie M, Rangaraju S (2017). Comparative environmental assessment of alternative fueled vehicles using a life cycle assessment. Transportation Research Procedia, 25: 3435–3445.
Zheng Y (2015). Trajectory data mining: An Overview. ACM Transactions on Intelligent Systems and Technology, 6: 29.
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