AI-Driven Scenarios for Urban Mobility: Quantifying the Role of ODE Models and Scenario Planning in Reducing Traffic Congestion
It addresses traffic congestion for urban policymakers, offering actionable strategies, but is incremental as it applies existing methods to a specific domain.
This paper tackles urban traffic congestion by modeling how AI adoption, like autonomous vehicles, affects congestion dynamics using ODEs and scenario planning, finding threshold levels of AI adoption needed for significant reduction.
Urbanization and technological advancements are reshaping urban mobility, presenting both challenges and opportunities. This paper investigates how Artificial Intelligence (AI)-driven technologies can impact traffic congestion dynamics and explores their potential to enhance transportation systems' efficiency. Specifically, we assess the role of AI innovations, such as autonomous vehicles and intelligent traffic management, in mitigating congestion under varying regulatory frameworks. Autonomous vehicles reduce congestion through optimized traffic flow, real-time route adjustments, and decreased human errors. The study employs Ordinary Differential Equations (ODEs) to model the dynamic relationship between AI adoption rates and traffic congestion, capturing systemic feedback loops. Quantitative outputs include threshold levels of AI adoption needed to achieve significant congestion reduction, while qualitative insights stem from scenario planning exploring regulatory and societal conditions. This dual-method approach offers actionable strategies for policymakers to create efficient, sustainable, and equitable urban transportation systems. While safety implications of AI are acknowledged, this study primarily focuses on congestion reduction dynamics.