The GPS tracking data from citizens volunteering their data from using applications on their "smartphones" to record where they bike offers researchers and practitioners a data source of great potential for use in transportation demand modeling. The literature in how to use and work with GPS data continues to grow. This paper is particularly interested in the role GPS bicycle tracking data has in the trip generation and trip distribution steps commonly used by metropolitan planning organizations. Rather than taking the approach of using GPS data to develop models though, this paper takes the approach of evaluating how the GPS data collected, presumably from a convenience or sometimes random sample, compares with current trip generation and distribution estimates. This is an extensive research effort that involves determining how to prepare the data such that the resulting dataset can be considered a sample dataset suitable for comparison with a MPO's model estimates for the population (for bike trips). Following this preparation process, this research explores the similarities and differences in the GPS sample dataset and the MPO population estimate dataset, such as the origins and destinations of the trips within the traffic analysis zone (TAZ) structure of the regional MPO. This research uses "Cycletracks" GPS data collected by volunteers that biked (not randomly selected) in Austin, Texas. An interesting finding to date is that for some TAZs, the GPS count of trips exceeds that of the population estimate from the MPO model, which is explored in more depth. The goals of this paper are to find out what GPS bicycle tracking data can tell us about current modeling estimation efforts and to consider how to move forward with incorporating GPS bicycle data into the transportation demand modeling process.