  Global positioning system (GPS) trajectories recorded by mobile phones or
action cameras offer valuable insights into sustainable mobility, as they
provide fine-scale spatial and temporal characteristics of individual travel.
However, the high volume, noise, and lack of semantic information in this data
poses challenges for storage, analysis, and applications. To address these
issues, we propose an end-to-end pipeline named CycleTrajectory for processing
high-sampling rate GPS trajectory data from action cameras, leveraging
OpenStreetMap (OSM) for semantic enrichment. The methodology includes (1) Data
Preparation, which includes filtration, noise removal, and resampling; (2) Map
Matching, which accurately aligns GPS points with road segments using the OSRM
API; (3) OSM Data integration to enrich trajectories with road infrastructure
details; and (4) Variable Calculation to derive metrics like distance, speed,
and infrastructure usage. Validation of the map matching results shows an error
rate of 5.64%, indicating the reliability of this pipeline. This approach
enhances efficient GPS data preparation and facilitates a deeper understanding
of cycling behavior and the cycling environment.
