Semi-Supervised travel mode detection from smartphone data
Why this work is in the frame
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Bibliographic record
Abstract
With the advent of the incorporation of GPS receivers and then GPS-enabled smartphones in transportation data collection, many studies have looked at how to infer meaningful information from this data. Research in this field has concentrated on the use of heuristics and supervised machine learning methods to detect: trip ends, trip itineraries, travel mode and trip purpose. All the methods used until now have depended on methods relying uniquely on fully-validated data. However, respondent burden associated with validation lowers participation rates and results in less reliable data. In this paper, we propose the use of semi-supervised methods that use both validated and un-validated data. We compare the accuracy for two popular supervised methods (i.e. decision tree and random forest) with a simple semi-supervised method (i.e. label propagation with KNN kernel). We use speed, duration and length of trip, as well as proximity of trip start and end points to the transit network to detect mode of transport. The results show that the semi-supervised method slightly outperforms the supervised methods in the presence of high portions of unvalidated data, while run-times of the more efficient of the two supervised methods was on average almost 16 times longer than the average run-times of the semi-supervised method.
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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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it