PTRAIL -- A python package for parallel trajectory data preprocessing
This provides a domain-specific tool for researchers and practitioners handling trajectory data, though it is incremental as it builds on existing preprocessing methods.
The authors tackled the challenge of preprocessing large and error-prone trajectory data by developing PTRAIL, a Python package that offers filtering, feature extraction, and interpolation, achieving faster performance compared to other libraries through parallel computation and vectorization.
Trajectory data represent a trace of an object that changes its position in space over time. This kind of data is complex to handle and analyze, since it is generally produced in huge quantities, often prone to errors generated by the geolocation device, human mishandling, or area coverage limitation. Therefore, there is a need for software specifically tailored to preprocess trajectory data. In this work we propose PTRAIL, a python package offering several trajectory preprocessing steps, including filtering, feature extraction, and interpolation. PTRAIL uses parallel computation and vectorization, being suitable for large datasets and fast compared to other python libraries.