Estimation of Green House Gas and Contaminant Emissions from Traffic by microsimulation and refined Origin-Destination matrices: a methodological approach

Abstract

The high levels of air contamination and presence of different pollutants are a large problem 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 strategies to help reduce and mitigate their global emissions. In terms of road transport, reductions in emissions could be achieved by replacing conventional vehicle technologies or by changing the travel patterns of individuals using a private vehicle as their primary means of transportation. However, accurately quantifying the emissions related to the urban traffic from multiple possible scenarios is a very complicated task, even when appropriate tools made for this purpose are available. Here we apply a scientifically rigorous protocol to accurately estimate greenhouse and other polluting gases. We describe the methodological steps we followed to analyse the vast quantities of data available from different heterogeneous sources. This data can aid decision-makers in planning better strategies for urban transportation. We used the origin-destination matrices already available for Valencia city (Spain), as well as historical information for their street induction-loops and the phases and times of their traffic light system as our input data for the traffic model. Rather than a brute-force algorithm, we used a fast-convergence Lagrangian algorithm model which deals with that vast quantities of information. Based on the elements mentioned above together with the statistics about the types of vehicles in the city by simulations the urban mobility city's traffic was reconstructed at different times to quantify the emissions produced with a high spatial and temporal resolution.

Keywords

• mobility
• demand modelling
• OD matrices
• CO2 emissions
• SUMO
• Lagrangian algorithm