A Digital Twin Framework for Urban Parking Management and Mobility Forecasting
Francesco Piccialli1,*, Sara Amitrano1, Donato Cerciello1, Anna Borrelli1, Edoardo Prezioso1, and Marzia Canzaniello1,*
1Department of Mathematics and Applications
R. Caccioppoli, M.O.D.A.L. - Mathematical mOdelling and Data AnaLysis research group, University of Naples Federico II, Italy
*{francesco.piccialli,marzia.canzaniello}@unina.it
This interactive web platform demonstrates the capabilities of the Digital Twin framework for urban parking management and mobility forecasting. Explore real-time data analytics, predictive modeling, and generative simulations tailored to enhance city planning and resource allocation. Dive into advanced features like what-if scenario testing, powered by machine learning and generative AI, and experience firsthand how the framework transforms data into actionable insights for smarter, more sustainable cities.