A human exposure-based traffic assignment model for minimizing fine particulate matter (PM2.5) intake from on-road vehicle emissions

Abstract

An exposure-based traffic assignment (TA) model and accompanying analysis framework have been developed to quantify primary and secondary fine particulate matter (PM2.5) exposure due to modeled on-road vehicle flow on a regional network at a high spatial resolution. The Chicago Metropolitan Area transportation network is used to demonstrate the model's decision-informing power. The study compares the spatially distributed exposure impacts due to traffic emissions of two TA optimization scenarios: a baseline user equilibrium with respect to travel time (UET) and a novel system optimal with respect to pollutant intake (SOI). The UET and SOI scenarios are developed through the use of (a) the TA model used for obtaining vehicle flow patterns and characteristics including emissions, (b) a source-receptor matrix for PM2.5 developed through a reduced-complexity air quality model to quantify primary and secondary PM2.5 concentrations across the exposure domain, (c) spatial analysis for assessing exposure profiles at the census tract level, and (d) a health impact model to quantify exposure damages. The SOI scenario yields a 9% - 10% total reduction in exposure damages, with the most impacted census tracts benefiting from up to 20% - 30% of reductions, but leads to a 16% increase in travel time costs. Further reduction to PM2.5 exposure by the SOI is hindered by network constraints, where travel demand in populous areas around the network must still be satisfied. The model can be used to systematically quantify the mitigation potential of different transportation exposure reduction strategies, to assess the exposure impacts of newly developed transportation infrastructure, and to address the equity implications of PM2.5 exposure from traffic, all under realistic system behavior and bounded by actual system constraints.