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Intense vehicular traffic is recognized as a global societal problem, with amultifaceted influence on the quality of life of a person. IntelligentTransportation Systems (ITS) can play an important role in combating suchproblem, decreasing pollution levels and, consequently, their negative effects.One of the goals of ITSs, in fact, is that of controlling traffic flows,measuring traffic states, providing vehicles with routes that globally pursuelow pollution conditions. How such systems measure and enforce given trafficstates has been at the center of multiple research efforts in the past fewyears. Although many different solutions have been proposed, very limitedeffort has been devoted to exploring the potential of social network analysisin such context. Social networks, in general, provide direct feedback frompeople and, as such, potentially very valuable information. A post that tells,for example, how a person feels about pollution at a given time in a givenlocation, could be put to good use by an environment aware ITS aiming atminimizing contaminant emissions in residential areas. This work verifies thefeasibility of using pollution related social network feeds into ITSoperations. In particular, it concentrates on understanding how reliable suchinformation is, producing an analysis that confronts over 1,500,000 posts andpollution data obtained from on-the- field sensors over a one-year span.

On the Feasibility of Social Network-based Pollution Sensing in ITSs