Path segmentation for movement trajectories with irregular sampling frequency using space-time interpolation and density-based spatial clustering

Path segmentation methods have been developed to distinguish stops and moves along movement trajectories. However, most studies do not focus on handling irregular sampling frequency of the movement data. This article proposes a four-step method to handle various time intervals between two consecutive records, including parameter setting, space-time interpolation, density-based spatial clustering, and integrating the geographic context. The article uses GPS tracking data provided by HOURCAR, a non-profit car-sharing service in Minnesota, as a case study to demonstrate our method and present the results. We also implement the DB-SMoT algorithm as a comparison. The results show that our four-step method can handle various time intervals between consecutive records, group consecutive stops close to each other, and distinguish different types of stops and their inferred activities. These results can provide novel insights into car-sharing behaviors such as trip purposes and activity scheduling.