Traffic-related Air Pollution in an Urban Environment: a KBDSS Improving the Decisional Context

Recent decades have seen very significant and profound changes to cities. The research we reported here deals with the growing problem of air pollution caused by traffic flows in a city, which demands emergency solutions as well as medium and long-term strategies. This situation challenges the usual decision-making procedures in urban organisations. Our research has the goal of supporting the daily activity of decision-makers in the municipal offices by means of a Decision Support System (DSS) based on the knowledge available, both documentary and of local experts. Then, designing the architecture of such DSS was the main operational goal of the research. We thought that using our DSS could help create a system able to support the decision-making activities of people in the municipal offices, for routine as well as for strategic actions, while also improving their ability to recognise, understand and represent problems. The research produced a range of different outputs, some related to the cognitive dimension and others to the potential of the DSS approach. The most significant results were those referring to: knowledge acquisition, knowledge representation, formalisations in terms of decision-routine production, and, finally, organisational and machine learning.