From GPS traces to individual emission exposure: a data-driven four-step process

Vehicular traffic is one of the major sources of air pollution in urban settings, making it essential to clearly understand how much and where vehicle emissions impact residents. Estimating vehicular pollution using GPS trajectories and microscopic models is getting more popular as this method has several advantages compared to other approaches. However, GPS data sources usually cover only a small sample of actual traffic, making current approaches unable to provide emission estimates for the whole road network. Moreover, to understand how much of these emissions reach different locations, a dispersion model should be applied, and quantifying their effect on individuals requires considering where they stay and/or how they move. Therefore, in this paper, we propose a four-step process that elaborates on raw, incomplete emission estimates and (i) first, estimates initial emissions from GPS data, (ii) estimates emission concentrations for the missing road segments, (iii) further processes the emission data to consider air dispersion, and (iv) computes the expected exposure to emissions of individuals in several use cases, involving both public buildings (e.g. schools) and pedestrian mobility. The experiments are based on a sample of vehicular GPS data in two Italian cities.