Estimation of Green House Gas and ContaminantEmissions from Traffic by microsimulation and refinedOrigin-Destination matrices: a methodological approach

The high levels of air contamination and presence of different pollutants are a largeproblem in most of the cities in which road transport is the primary source of emissions.The governments of more than 100 countries are adopting different policies and strategiesto help reduce and mitigate their global emissions. In terms of road transport, reductionsin emissions could be achieved by replacing conventional vehicle technologies or by chang-ing the travel patterns of individuals using a private vehicle as their primary means oftransportation. However, accurately quantifying the emissions related to the urban trafficfrom multiple possible scenarios is a very complicated task, even when appropriate toolsmade for this purpose are available. Here we apply a scientifically rigorous protocol toaccurately estimate greenhouse and other polluting gases. We describe the methodologicalsteps we followed to analyse the vast quantities of data available from different heteroge-neous sources. This data can aid decision-makers in planning better strategies for urbantransportation. We used the origin-destination matrices already available for Valencia city(Spain), as well as historical information for their street induction-loops and the phasesand times of their traffic light system as our input data for the traffic model. Rather than abrute-force algorithm, we used a fast-convergence Lagrangian algorithm model which dealswith that vast quantities of information. Based on the elements mentioned above togetherwith the statistics about the types of vehicles in the city by simulations the urban mobilitycity’s traffic was reconstructed at different times to quantify the emissions produced witha high spatial and temporal resolution