Freight Performance Measures (FPM) are of interest to transportation planning agencies. One of the key tools that aids in the study of freight system activity is the data from Global Positioning System (GPS) devices located in trucks and cars. While commercially available GPS data has a common basic output format, the level of aggregation of the raw data, impacts the data’s ultimate usability and applications. This paper categorizes the different level of GPS data – from raw to highly aggregate and highlights the different strength, weakness, and applications of the data. Based on the insights learned from previous studies related to GPS data types, the authors make recommendations for how to match the GPS data to different analytical needs.

Truck probe data collected by global positioning system (GPS) devices has gained increased attention as a source of truck mobility data, including measuring truck travel time reliability. Most reliability studies that apply GPS data are based on travel time observations retrieved from GPS data. The major challenges to using GPS data are small, nonrandom observation sets and low reading frequency. In contrast, using GPS spot speed (instantaneous speed recorded by GPS devices) directly can address these concerns. However, a recently introduced GPS spot-speed-based reliability metric that uses speed distribution does not provide a numerical value that would allow for a quantitative evaluation. In light of this, the research described in this article improves the current GPS spot speed distribution-based reliability approach by calculating the speed distribution coefficient of variation. An empirical investigation of truck travel time reliability on Interstate 5 in Seattle, WA, is performed. In addition, correlations are provided between the improved approach and a number of commonly used reliability measures. The reliability measures are not highly correlated, demonstrating that different measures provide different conclusions for the same underlying data and traffic conditions. The advantages and disadvantages of each measure are discussed and recommendations of the appropriate measures for different applications are presented.