Staypoint Detection from Noisy Trajectory Data [Experiment Paper]

2026-07-21

AI Summary

This study addresses the challenge of systematically evaluating stay-point detection algorithms under noisy trajectory conditions, which has been hindered by the absence of publicly available benchmark datasets with ground-truth annotations. To bridge this gap, we construct the first large-scale synthetic trajectory dataset annotated with real-world stay points and propose both a novel unsupervised and a supervised detection algorithm. Leveraging this dataset alongside a noise-robustness evaluation framework, we conduct a comprehensive assessment of nine representative algorithms. Our results reveal that state-of-the-art methods suffer significant performance degradation under realistic noise, whereas the proposed approaches substantially improve detection accuracy, with the supervised variant notably outperforming existing baselines.

Abstract

Detecting staypoints from raw trajectory data is fundamental to numerous spatial computing applications. This process transforms raw numeric sequences of geolocations into semantically meaningful locations, such as homes, workplaces, or restaurants. Despite its importance for semantic trajectory analysis, staypoint detection lacks standard benchmarks, and existing algorithms have never been systematically evaluated. This gap persists because no publicly available datasets provide both raw individual trajectories and ground-truth staypoint annotations. This benchmark paper addresses this limitation with two key contributions: (1) we introduce 16 large-scale simulated datasets capturing thousands of agents with annotated staypoints across varying trajectory noise levels, and (2) we evaluate nine staypoint detection algorithms-including both state-of-the-art and novel methods-to analyze their robustness to noise. Our evaluation reveals that existing state-of-the-art algorithms perform poorly under realistic noise conditions. Conversely, our proposed unsupervised methods yield substantial improvements, while supervised approaches drastically outperform existing baselines. While these results are very promising, these datasets and methods are only meant as starting points for future research in staypoint detection.

Problem

• staypoint detection
• trajectory data
• noise robustness
• benchmark dataset
• ground-truth annotation

Innovation

• staypoint detection
• trajectory noise
• benchmark dataset
• unsupervised method
• semantic trajectory analysis