OUTFIT – crOwdsoUrced daTa Feeding noise maps in dIgital Twins: Road traffic dynamic data modeling for urban noise representation in digital twins

OUTFIT – crOwdsoUrced daTa Feeding noise maps in dIgital Twins: Road traffic dynamic data modeling for urban noise representation in digital twins

Duration: 28 Sep 2023 – 27 Sep 2025 (2 years)

Principal Investigator: Elena Ascari (IPCF)

Other research units (contact person): University of Pisa (Gabriele Mencagli); University of Salento (Antonella Longo)

ERC Sectors: PE6_12 Scientific computing, simulation and modelling tools; PE6_2 Distributed systems, parallel computing, sensor networks, cyber-physical systems; PE6_10 Web and information systems, data management systems, information retrieval and digital libraries, data fusion

Keywords: Road Traffic Noise RTN; Crowd-sourced; Digital Twin DT; High Performance Computing HPC; Traffic model; Big Data

OUTFIT implements a Digital Twin (DT) of an urban area to deliver dynamic Road Traffic Noise (RTN) estimation based on crowd-sourced data, developing a tool able to rely on live timing changes thanks to High Performance Computing (HPC) methods. A new method will be developed to derive noise correction factors to be applied to real time input data to provide RTN output.
The DT will not only visualize noise levels but also citizens’ complaints retrieved from social networks and from a specific app that OUTFIT will develop for the experimental phase. Feedback data will be delivered in the DT, after stream optimization, to drive policies, i.e. offering a further perspective other than quantitative levels.
OUTFIT delivers the optimization of traffic data and social data in DT that could fit various environmental models, e.g. it can be even used for air pollution models and a site-independent method for RTN allowing maps based on crowd-sourced data and derived correction factors.

Other IPCF people involved in the research activities: P. Gorrasi