Efficient and reliable mobility systems are at the foundations of the long-term sustainable environmental, economic, and social development of large urban areas around the globe.
Nonparametric forecasting of pick-ups and drop-offs of shared bikes in the city of Milan.
Modern human mobility systems are built on two pillars: mobility services (which collect the mobility offer for people and their goods) and mobility infrastructures (which represents the physical structure on which mobility services operate). Both mobility services and infrastructures were traditionally designed relying either on theoretical models (e.g., gravitational models) or on public surveys (e.g., censimento nazionale ISTAT) aiming at estimating the mobility demand for any possible origin-destination pair.
In the last decade, we observed the advent and massive diffusion of mobility data collectable through modern technologies which can possibly provide a complete and real-time picture of human mobility. This technological shift has challenged the traditional static and top-down approach to the design mobility services and infrastructures and pushed public and private actors to test an alternative dynamic and bottom-up design approach leveraging the information derived from these new big data.
The mission of the MobiMOX group on sustainable mobility is contributing to this global shift by creating new methods and data analysis algorithms (especially but non exclusively in the realm of Functional Data Analysis, Network Analysis, and Nonparametric Statistics) supporting the creation of a competitive advantage or a repositioning of institutions and private firms operating in the sector. Our research is naturally interdisciplinary with long lasting collaborations with different departments of Politecnico di Milano.