A city digital twin for a sustainable mobility: big data analytics and predictive models

City digital twin is fundamental to realize the transition to a sustainable mobility in the next future. This is not a digital copy of the city, but a system of data-driven dynamical models, that describe the mobility on the transport networks using different spatial and time scale levels, from the traffic flows dynamics up to the individual behaviour. Indeed, the mobility demand is realized in a multilayer structure of mobility networks and the models must integrate the different mobility types distinguishing different individual behaviours. Moreover, the dynamical models must give predictions on possible scenarios for the city mobility when new infrastructures are built, or new policies for the development of a sustainable mobility are implemented. The first step toward the realization of a digital twin is to collect ’big data’ to get information on the present mobility state of a city (i.e. the congestion degree) distinguishing the different types of mobility, and to model the mobility demand. The main data sources are ICT data at individual level or real time data from many distributed sensors on the road network. In the seminar we illustrate the recent results on the use of GPS data from mobile phones to distinguish different types of mobility in a city, and the possibility of building dynamical models to predict the traffic flows on a road network using the collected data from distributed sensors.

Zoom: https://tinyurl.com/bdfcrbhp

Detalls de contacte:

Jose Javier Ramasco

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