  The improvement of air-quality in urban areas is one of the main concerns of
public government bodies. This concern emerges from the evidence between the
air quality and the public health. Major efforts from government bodies in this
area include monitoring and forecasting systems, banning more pollutant motor
vehicles, and traffic limitations during the periods of low-quality air. In
this work, a proposal for dynamic prices in regulated parking services is
presented. The dynamic prices in parking service must discourage motor vehicles
parking when low-quality episodes are predicted. For this purpose, diverse deep
learning strategies are evaluated. They have in common the use of collective
air-quality measurements for forecasting labels about air quality in the city.
The proposal is evaluated by using economic parameters and deep learning
quality criteria at Madrid (Spain).
