Moving towards the digital transition of the mobility sector by putting in place the necessary sensing and computing infrastructure and intelligence, cities can obtain real-time traffic information which cannot be captured through simulated traffic models. The digitalization of mobility must leverage disruptive ICT technologies, such as Internet of the Things (IoT), big data analytics and Artificial Intelligence (AI).

AIR-URBAN project proposes a methodology to enhance air quality monitoring and forecasting by incorporating observations from real time traffic data. This is achieved by implementing the following key steps:

• extract traffic data (i.e., types of vehicles, and their speed and acceleration) and traffic events (e.g., congestion) from video sources,
• include the traffic data into a model for the estimation of vehicular emissions,
• feed the estimated emissions into a model for AQ forecasting, thus obtaining more accurate values,
• develop AI methods for a more fine-grained AQ estimation.

The outcomes of the project can be also applied in any smart city services such as smart mobility, for example.