PTRAIL -- A python package for parallel trajectory data preprocessing

2021/08/26 by Salman Haidri, Haidri, Salman, Yaksh J. Haranwala +9 · 2 citations
Computer Science · #Anomaly Detection Techniques and Applications #Artificial Intelligence (cs.AI) #Data Management and Algorithms #Distributed #FOS: Computer and information sciences #Parallel #Time Series Analysis and Forecasting #and Cluster Computing (cs.DC)

paper · pdf · doi:10.48550/arxiv.2108.13202

openalex publication_date 2021/08/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Cited by

• An experimental study of existing tools for outlier detection and cleaning in trajectories
• Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach

Related

• yupi: Generation, Tracking and Analysis of Trajectory data in Python
• AugmentTRAJ: A framework for point-based trajectory data augmentation
• dynsight: an Open Python Platform for Simulation and Experimental Trajectory Data Analysis
• TrajPy: empowering feature engineering for trajectory analysis across domains
• Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach