GeoSDVA: A Semi-Supervised Dirichlet Variational Autoencoder Model for Transportation Mode Identification
Bibliographic record
Abstract
Inferring the transportation modes of travelers is an essential part of intelligent transportation systems. With the development of mobile services, it is easy to effectively obtain massive location readings of travelers with GPS-enabled smart devices, such as smartphones. These readings make understanding human activities very convenient. Therefore, how to automatically infer transportation modes from these massive readings has come into the spotlight. The existing methods for transportation mode identification are usually based on supervised learning. However, the raw GPS readings do not contain any labels, and it is expensive and time-consuming to annotate sufficient samples for training supervised learning-based models. In addition, not enough attention is paid to the problem that GPS readings collected in urban areas are affected by surrounding geographic information (e.g., the level of road transportation or the distribution of stations). To solve this problem, a geographic information-fused semi-supervised method based on a Dirichlet variational autoencoder, named GeoSDVA, is proposed in this paper for transportation mode identification. GeoSDVA first fuses the motion features of the GPS trajectories with the nearby geographic information. Then, both labeled and unlabeled trajectories are used to train the semi-supervised model based on the Dirichlet variational autoencoder architecture for transportation mode identification. Experiments on three real GPS trajectory datasets demonstrate that GeoSDVA can train an excellent transportation mode identification model with only a few labeled trajectories.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".