ROADSTER: ROAd Digital Sustainable Twins in Emilia-Romagna

ROAd Digital Sustainable Twins in Emilia-Romagna
Thematic area: Digital society, Projects
Financing: IFAB call for projects
Enabling Technology: Machine Learning

The “ROADSTER – ROAd Digital Sustainable Twins in Emilia-Romagna” project goes beyond the state-of-the-art and combine multi-modal data in the complex and dynamic system of industrial vehicular mobility.


  • Develop innovative AI solutions to manage mobility in industrial areas.
  • Define a new theoretical framework for Digital and Sustainable Twins in the field of transport infrastructure.

The initial challenge

The complexity of the management of the transport system in industrial areas and the risk conditions given by traffic and the movement of workers to/from/to the workplace.

The solution

ROADSTER aims at investigating and developing new state-of-the-art AI solutions in Computer Vision and Deep Learning for assessing a new framework of “Digital and Sustainable Twin” of the complex ecosystem of roads and transportation facilities in industrial areas. The proposal is very innovative as it shifts the focus from the classical “Smart city” environment to that of industrial production areas, which are very critical for our territory in terms of traffic intensity, pollution, and workers’ safety. The goal is to provide new data, processed online and off-line with new Italian Services regarding:

  1. road conditions and anomalies around industrial sites;
  2. personalized services about traffic conditions for optimizing transport scheduling;
  3. the improvement of workers safety during their journeys to/from the workplace.

ROASTER has two key aspects: it aims to provide valuable scientific results in AI and will be a proof-of-concept of a very ambitious and largely scalable project to create an Italian answer in the management of mobility data, devoted to the support of industry production and of the ecological transition.


  • Production of new data, processed on-line and off-line on the viability and anomalies around industrial sites.
  • Possibility to create customized services on traffic conditions for the optimization of transport scheduling.
  • Improvement of traffic conditions and facilitation of transport planning.
  • Improving the safety of workers during their commute to/from the workplace.
  • Expand project activities with built-in scalability.
  • Integration and optimization of AI components to improve category recognizability.


For further information, please contact:

Sustainable Development Goals

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