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.