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Urban Mobility: AI, ODE-Based Modeling, and Scenario Planning

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  • Katsiaryna Bahamazava

    (Department of Mathematical Sciences G.L. Lagrange, Politecnico di Torino, Italy, iLaVita Nonprofit Foundation, Italy - USA)

Abstract

Urbanization and technological advancements are reshaping the future of urban mobility, presenting both challenges and opportunities. This paper combines foresight and scenario planning with mathematical modeling using Ordinary Differential Equations (ODEs) to explore how Artificial Intelligence (AI)-driven technologies can transform transportation systems. By simulating ODE-based models in Python, we quantify the impact of AI innovations, such as autonomous vehicles and intelligent traffic management, on reducing traffic congestion under different regulatory conditions. Our ODE models capture the dynamic relationship between AI adoption rates and traffic congestion, providing quantitative insights into how future scenarios might unfold. By incorporating industry collaborations and case studies, we offer strategic guidance for businesses and policymakers navigating this evolving landscape. This study contributes to understanding how foresight, scenario planning, and ODE modeling can inform strategies for creating more efficient, sustainable, and livable cities through AI adoption.

Suggested Citation

  • Katsiaryna Bahamazava, 2024. "Urban Mobility: AI, ODE-Based Modeling, and Scenario Planning," Papers 2410.19915, arXiv.org.
    • Handle: RePEc:arx:papers:2410.19915
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    File URL: http://arxiv.org/pdf/2410.19915
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    References listed on IDEAS

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    1. He, Xiaoping & Yu, Yuxuan & Jiang, Shuo, 2023. "City centrality, population density and energy efficiency," Energy Economics, Elsevier, vol. 117(C).
    2. Auwal Alhassan Musa & Salim Idris Malami & Fayez Alanazi & Wassef Ounaies & Mohammed Alshammari & Sadi Ibrahim Haruna, 2023. "Sustainable Traffic Management for Smart Cities Using Internet-of-Things-Oriented Intelligent Transportation Systems (ITS): Challenges and Recommendations," Sustainability, MDPI, vol. 15(13), pages 1-15, June.
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