A new methodology and algorithms were developed to combine diverse data sources and to estimate the impacts of recurrent and non-recurrent congestion on freight movements’ reliability and delays, costs, and emissions. The results suggest that traditional traffic sensor data tend to underestimate the impacts of congestion on commercial vehicles travel times and variability. This research also shows that congestion is not only detrimental for carriers and shippers costs but also for the planet due to major increases in GHG emissions and for the local community due to large increases in NOx, PM, and other harmful pollutants. The methodologies developed throughout this work have the potential to provide useful freight operation and performance data for transportation decision makers to incorporate freight performance measures into the planning process. The development of such tools as those presented here, are an integral step in the processing of disaggregate truck data to gather information necessary for producing performance measures for the freight vehicles. This first part of this report focuses on performance measures on the Portland metropolitan region and the second part on longer freeway segments for the more than 300 miles of Interstate Highway 5 in Oregon. In the Portland Metropolitan Area, this research focused on the development of multi-criteria tools for measuring and analyzing the impacts of recurring and non-recurring congestion on freight corridors. Unlike previous studies, this work employs several distinct data sources to analyze the impacts of congestion on Interstate 5 (I-5) in the Portland Metropolitan Area: global positioning system (GPS) data from commercial trucks and Oregon DOT corridor travel-time loop data and incident data. In addition to studying a pre-defined urban corridor, this research was expanded to investigate longer corridors, using programming logic and available GPS data from commercial trucks to segment the roadway into manageable, coherent study areas. Long freight corridors are comprised of segments with potentially different reliability characteristics. This research has developed a programming logic that uses available truck GPS data to: (a) identify corridor natural segments or regions (urban centers, interstate junctions, rural areas), and (b) estimate corridor wide impacts of travel time unreliability. The case study presented within this report investigates the Interstate 5 (I-5) corridor in Oregon. After identifying corridor segments, this research applies statistical techniques to compute vehicle travel time and reliability for freight movements within each segment. The proposed methodology has been successful in identifying distinct segments and characteristics of travel time reliability in freight corridors. This travel time information was then used to compute cost impacts within rural and urban areas along the I-5 corridor.