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Quantifying the relationship between material and construction (M&C) variables and pavement performance is an on‐going important research area. It is, however, realized that deriving such relationships is too complex and too poorly understood to develop using traditional statistical methods. Therefore, this study is focused on the analysis of a data set from the long‐term pavement performance (LTPP) database to quantify the contribution of M&C variables of asphalt concrete on pavement performance (i.e., international roughness indicator) using a back‐propagation neural network (BPNN) algorithm. It was found that by using sensitivity analysis neural network trained with optimal number of epochs could be used effectively for better understanding of the factors controlling overall performance indicators, establishing quantitative functions to weigh the role of such factors, and for use in performance‐related specifications.

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We present an examination of the potential emissions and air quality benefits of shifting freight from truck to rail in the upper Midwestern United States. Using a novel, freight-specific emissions inventory (the Wisconsin Inventory of Freight Emissions, WIFE) and a three-dimensional Eulerian photochemical transport model (the Community Multiscale Air Quality Model, CMAQ), we quantify how specific freight mode choices impact ambient air pollution concentrations. Using WIFE, we developed two modal shift scenarios: one focusing on intraregional freight movements within the Midwest and a second on through-freight movements through the region. Freight truck and rail emissions inventories for each scenario were gridded to a 12 km × 12 km horizontal resolution as input to CMAQ, along with emissions from all other major sectors, and three-dimensional time-varying meteorology from the Weather Research and Forecasting model (WRF). The through-freight scenario reduced monthly mean (January and July) localized concentrations of nitrogen dioxide (NO2) by 28% (-2.33 ppbV) in highway grid cells, and reduced elemental carbon (EC) by 16% (-0.05 μg/m(3)) in highway grid cells. There were corresponding localized increases in railway grid cells of 25% (+0.83 ppbV) for NO2, and 22% (+0.05 μg/m(3)) for EC. The through-freight scenario reduced CO2 emissions 31% compared to baseline trucking. The through-freight scenario yields a July mean change in ground-level ambient PM2.5 and O3 over the central and eastern part of the domain (up to -3%).

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