# Few Shot Semi-Supervised Learning for Abnormal Stop Detection from Sparse GPS Trajectories

arXiv: https://arxiv.org/abs/2510.12686 · HTML: https://mlsanity.com/paper/2510.12686/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 36 references that are in the map.
- First posted on arXiv 14 Oct 2025.

## Builds on (most cited first, 7 of 7)

- [SAD: Semi-Supervised Anomaly Detection on Dynamic Graphs](https://mlsanity.com/paper/2305.13573/) (2023)
- [Generative Semi-supervised Graph Anomaly Detection](https://mlsanity.com/paper/2402.11887/) (2024)
- [Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly Detection](https://mlsanity.com/paper/2307.08390/) (2023)
- [Finding A Taxi with Illegal Driver Substitution Activity via Behavior Modelings](https://mlsanity.com/paper/2404.11844/) (2024)
- [Detecting Contextual Network Anomalies with Graph Neural Networks](https://mlsanity.com/paper/2312.06342/) (2023)
- [Semi-supervised Anomaly Detection with Extremely Limited Labels in Dynamic Graphs](https://mlsanity.com/paper/2501.15035/) (2025)
- [Enhancing stop location detection for incomplete urban mobility datasets](https://mlsanity.com/paper/2407.11579/) (2024)

## Built on by (most cited first, 1 of 1)

- [Artificial Intelligence for Understanding and Managing Transportation Behavior in Sustainable Smart Cities](https://mlsanity.com/paper/2607.17694/) (2026)

## Abstract (arXiv)

Abnormal stop detection (ASD) in intercity coach transportation is critical for ensuring passenger safety, operational reliability, and regulatory compliance. However, two key challenges hinder ASD effectiveness: sparse GPS trajectories, which obscure short or unauthorized stops, and limited labeled data, which restricts supervised learning. Existing methods often assume dense sampling or regular movement patterns, limiting their applicability. To address data sparsity, we propose a Sparsity-Aware Segmentation (SAS) method that adaptively defines segment boundaries based on local spatial-temporal density. Building upon these segments, we introduce three domain-specific indicators to capture abnormal stop behaviors. To further mitigate the impact of sparsity, we develop Locally Temporal-Indicator Guided Adjustment (LTIGA), which smooths these indicators via local similarity graphs. To overcome label scarcity, we construct a spatial-temporal graph where each segment is a node with LTIGA-refined features. We apply label propagation to expand weak supervision across the graph, followed by a GCN to learn relational patterns. A final self-training module incorporates high-confidence pseudo-labels to iteratively improve predictions. Experiments on real-world coach data show an AUC of 0.854 and AP of 0.866 using only 10 labeled instances, outperforming prior methods. The code and dataset are publicly available at hrefhttps://github.com/pangjunbiao/Abnormal-Stop-Detection-SSL.git