Mobile-source emissions are pivotal in quantifying the negative externalities of surface transportation, such as environmental pollution and climate-change, and in evaluating low-carbon traffic strategies. In such assessments, it is important to avoid prospective policy shortcomings. Hence, a wide range of sensitivities of mobile-source emissions must be understood, particularly from a traffic modeling standpoint. This paper takes a step in that direction and explores the effects of certain supply-side network attributes on emissions. Three key elements are investigated: level-of-detail of traffic activity, link speeds in the network, and link lengths. Both aggregated (hourly) and fine-grained (per-second) traffic activities are modeled using a simulation-based dynamic traffic assignment tool. Emissions are modeled using US Environmental Protection Agency's Motor Vehicle Emissions Simulator (MOVES). System-wide estimates of five criteria pollutants (CO, NO2, PM10, PM2.5, and SO2) and greenhouse-gases (CO2) are developed for a weekday morning peak-hour modeling period. Numerical experiments on a rapidly growing county in Central Texas, US, indicate that emission estimates are sensitive to all the aforementioned supply-side variables. Most notably, median network-wide estimates are found to increase in magnitude with aggregation of traffic activity and speeds. Effects of link lengths appear to be more prominent in high-speed traffic corridors, such as restricted-access highways, than low-speed unrestricted-access arterials. The latter, however, witness more traffic dynamics and subsequently contribute more to deviation in emission estimates across levels-of-detail. The findings highlight the need to be mindful of such physical sensitivities of emissions while enacting policy decisions, which frequently rely on network-based regional emissions inventories.
Bibliographical noteFunding Information:
An earlier version of this paper was peer-reviewed and accepted at WCTR-2016 Shanghai, and the authors acknowledge WCTRS for the opportunity to publish this work.
- Dynamic traffic assignment
- Low-carbon traffic
- Mobile-source emissions
- Network-based emissions