{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/ptrail-a-python-package-for-parallel-1","title":"PTRAIL - A python package for parallel trajectory data preprocessing","arxiv_id":null,"date":"2021-08-26","proceeding":"SoftwareX 2021 8","authors":["Salman Haidri","Yaksh J. Haranwala","Vania Bogorny","Chiara Renso","Vinicius Prado da Fonseca","Amilcar Soares"],"abstract":"Trajectory data represent a trace of an object that changes its position\r\nin 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 specifi\fcally tailored to preprocess trajectory data. In this work we propose PTRAIL, a python package o\u000bring several trajectory preprocessing steps, including \fltering, feature extraction, and interpolation. PTRAIL uses parallel computation and vectorization, being suitable for large datasets and fast compared to other python libraries.","url_abs":"https://arxiv.org/abs/2108.13202","url_pdf":"https://arxiv.org/pdf/2108.13202.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"ptrail-a-python-package-for-parallel-1","repo_url":"https://github.com/YakshHaranwala/PTRAIL","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":null,"task_name":"Position"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}