Person:

Lebrusan, Irene

With the increase of population living in urban areas, many transportation-related problems have grown very rapidly. Pollution causes many inhabitants health problems. A major concern for the International Community is pollution, which causes many inhabitants health problems. Accordingly, and under the risk of fines, countries are required to reduce noise and air pollutants. As a way to do so, road restrictions policies are applied in urban areas. Evaluating objectively the benefits of this type of measures is important to asses their real impact. In this work, we analyze the application of Madrid Central (MC), which is a set of road traffic limitation measures applied in the downtown of Madrid (Spain), by using smart city tools. According to our results, MC significantly reduces the nitrogen dioxide (NO2) concentration in the air and the levels of noise in Madrid, while not arising any border effect.

This article presents the application of data analysis and computational intelligence techniques for evaluating the air quality in the center of Madrid, Spain. Polynomial regression and deep learning methods to analyze the time series of nitrogen dioxide concentration, in order to evaluate the effectiveness of Madrid Central, a set of road traffic limitation measures applied in downtown Madrid. According to the reported results, Madrid Central was able to significantly reduce the nitrogen dioxide concentration, thus effectively improving air quality.