Integration of two models, Mobility Digital Twin and Water Digital Twin – Matera Case Study

The Urban Intelligence approach views the city as a complex system that needs to be studied through the interaction of its different subsystems. Such complexity is addressed also in the virtual dimension, through the construction of Urban Digital Twins that allow to understand, control, and optimize the urban dynamics according to multidimensional objectives.

In this context, we describe here a model to assess and evaluate the risks incurred by pedestrians and vehicles in a city under severe and extreme rainfall events that results in increasing of surface runoff, causing pluvial floods. This study is motivated by the increasing frequency of extreme events that seriously challenge the urban infrastructures in historical cities where urban design dates back centuries and constraints to structural modifications of the urban texture are often present.

The approach is based on the design and integration of two models: first, a traffic macro-simulation model that integrates multi-objective demand and resources in an optimal and automated way; such model, also referred to as the Mobility Digital Twin, can predict vehicle and pedestrian flows over the segments of the city network. Second, a model of water dynamics over the same city network (Water Digital Twin), based on the morphological structure of the territory and on the 3D urban model, that integrates a hydrological-hydraulic coupled model that is able, starting from predetermined rainfall events, to estimate the water levels and flow rates in each portion of the investigated territory of rainfall.

The two models are jointly used to create scenarios for different weather conditions, simulate recovery policies, identify the system’s bottlenecks and design evacuation strategies, both at the strategic and at the operational level. The results of the experimentation will be analyzed and implemented within the SIT. Specifically, with Intelligent SIT, we define a framework for integrating data from diverse sources, including informative, participatory, and human-centric data, as well as outputs from Thematic Digital Twins and other sources. To accurately represent complex systems, we rely on detailed maps and in-depth spatial analysis, made possible through the capabilities of the SIT.

A prototype application of the approach is developed for the City of Matera, within the Casa delle Tecnologie Emergenti project and the development of the city’s Urban Digital Twin. Preliminary results validate the potential contribution of the models adopted and have been used to support local authorities in the design of recovery strategies in the presence of extreme weather events and in the planning of  mitigation actions on the city road network.

Acknowledgments This research was supported by the “Casa delle Tecnologie Emergenti di Matera” project.