Performance metrics have typically focused at two main scales: a microscopic scale that focuses on specific locations, time-periods, and trips; and, a macroscopic scale that averages metrics over longer times, entire routes, and networks. When applied to networks, microscopic methodologies often have computational limitations while macroscopic methodologies ascribe artificial uniformity to non-uniform analysis areas. These limitations highlight the need for a middle approach. This research merges four common types of transit data collected over a full year. The resulting data set (120 GB) is aggregated to reduce computational requirements, yet preserves high granularity for statistical analysis. Routes are broken down into five to eight segments centered around scheduled timepoints and examined by hour to produce useful statistics that are calculated from and represent multiple bus trajectories. Factors identified at the microscopic scale (e.g. passenger movements, bus interactions at stops, travel times, travel speeds, etc.) may be examined for trends without the heavy computation burden required to process large microscopic datasets. Variable relationships and system performance measures at this mesoscopic level may be used to examine spatial and temporal variations in transit service, quantify congestion and associated costs to users and agencies, and compare travel patterns at the route and system level.