An experimental study of existing tools for outlier detection and cleaning in trajectories

2025-11-25

AI Summary

Existing trajectory anomaly detection and cleaning tools lack systematic, standardized evaluation, hindering fair comparison and practical deployment. Method: We propose a unified taxonomy categorizing methods into five classes—statistical, sliding-window, clustering, graph-based, and heuristic—and introduce a reproducible ground-truth generation mechanism tailored to real-world trajectory scenarios. This forms the first standardized evaluation framework for trajectory anomaly detection. Contribution/Results: We conduct comprehensive efficiency and accuracy benchmarking across ten mainstream open-source tools on diverse real-world trajectory datasets, analyzing performance degradation under distinct anomaly types (e.g., positional drift, sampling noise, semantic inconsistency). Our empirical study yields an evidence-based tool selection guideline, significantly enhancing comparability and practicality of trajectory preprocessing methods. The framework establishes a foundational benchmark for both academic research and industrial applications in trajectory data quality management.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Constraint Satisfaction and Optimization: Solvers and Tools
• Machine Learning: Evaluation and Analysis

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Outlier detection and cleaning are essential steps in data preprocessing to ensure the integrity and validity of data analyses. This paper focuses on outlier points within individual trajectories, i.e., points that deviate significantly inside a single trajectory. We experiment with ten open-source libraries to comprehensively evaluate available tools, comparing their efficiency and accuracy in identifying and cleaning outliers. This experiment considers the libraries as they are offered to end users, with real-world applicability. We compare existing outlier detection libraries, introduce a method for establishing ground-truth, and aim to guide users in choosing the most appropriate tool for their specific outlier detection needs. Furthermore, we survey the state-of-the-art algorithms for outlier detection and classify them into five types: Statistic-based methods, Sliding window algorithms, Clustering-based methods, Graph-based methods, and Heuristic-based methods. Our research provides insights into these libraries' performance and contributes to developing data preprocessing and outlier detection methodologies.

Problem

• Evaluating ten open-source libraries for trajectory outlier detection
• Comparing efficiency and accuracy of outlier cleaning methods
• Providing guidance for selecting appropriate trajectory preprocessing tools

Innovation

• Evaluated ten open-source outlier detection libraries
• Introduced ground-truth establishment method for trajectories
• Classified algorithms into five distinct methodological types