Staypoint Detection from Noisy Trajectory Data [Experiment Paper]
2026-07-21 • Machine Learning
Machine LearningComputational Geometry
AI summaryⓘ
The authors focus on finding 'staypoints,' which are places people spend time at, like home or work, from GPS-like location data. They note that current methods for doing this haven't been properly tested because there were no good datasets with known answers. To fix this, they created 16 big simulated datasets with labeled staypoints and tested nine different algorithms. Their findings show existing popular methods struggle with noisy data, while their new unsupervised and supervised methods work better. These contributions provide a foundation for improving staypoint detection in the future.
staypoint detectiontrajectory datasemantic trajectorygeolocationbenchmark datasetnoise robustnessunsupervised learningsupervised learningspatial computing
Authors
Lance Kennedy, Hossein Amiri, Yueyang Liu, Riyang Bao, Hanqi Chen, Mohammad Hashemi, Ruochen Kong, Xiaotong Liu, Joon-Seok Kim, Shengpu Tang, Liang Zhao, Andreas Züfle
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.