A Data Augmentation Algorithm for Trajectory Data
Bibliographic record
Abstract
The growing prevalence of location-based devices has resulted in a significant abundance of location data from various tracking vendors. Nevertheless, a noticeable deficit exists regarding readily accessible, extensive, and publicly available datasets for research purposes, primarily due to privacy concerns and ownership constraints. There is a pressing need for expansive datasets to advance machine learning techniques in this domain. The absence of such resources currently represents a substantial hindrance to research progress in this field. Data augmentation is emerging as a popular technique to mitigate this issue in several domains. However, applying state-of-the-art techniques as-is proves challenging when dealing with trajectory data due to the intricate spatio-temporal dependencies inherent to such data. In this work, we propose a novel strategy for augmenting trajectory data that applies a geographical perturbation on trajectory points along a trajectory. Such a perturbation results in controlled changes in the raw trajectory and, consequently, causes changes in the trajectory feature space. We test our strategy in two trajectory datasets and show a performance improvement of approximately 20% when contrasted with the baseline. We believe this strategy will pave the way for a more comprehensive framework for trajectory data augmentation that can be used in fields where few labeled trajectory data are available for training machine learning models.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".