Global Navigation Satellite System (GNSS) data is an inexpensive and ubiquitous source of activity data. Global Positioning System (GPS) is an example of such data. Although there have been several studies about inferring device activity using GPS data from a consumer device, freight GPS data presents unique challenges for example having low and variable frequency, long transmission gaps, and frequent and unpredictable device ID resetting for preserving privacy. This study aims to provide an end-to-end, generic data analytical framework to infer multiple aspects of truck activity such as stops, trips, and tours. We use popular existing methods to construct the data processing pipeline and provide insights into their practical usage. We also propose improved data filters to different aspects of the data processing pipeline to address challenges found in privacy-preserving freight GPS data. We use freight data across four weeks from the greater Philadelphia region with variable transmission frequency ranging from one second to several hours to perform experiments and validate our methods. Our findings indicate that auxiliary information such as land use can be helpful in fine tuning stop inference, but spatio-temporal information contained in timestamped GPS pings is still the most powerful source of false stop identification. We also find that a combination of simple clustering techniques can provide a way to perform fast and reasonable clustering of the same stop.