Ensemble Convolutional Neural Networks for Mode Inference in Smartphone\n Travel Survey
Bibliographic record
Abstract
We develop ensemble Convolutional Neural Networks (CNNs) to classify the\ntransportation mode of trip data collected as part of a large-scale smartphone\ntravel survey in Montreal, Canada. Our proposed ensemble library is composed of\na series of CNN models with different hyper-parameter values and CNN\narchitectures. In our final model, we combine the output of CNN models using\n"average voting", "majority voting" and "optimal weights" methods. Furthermore,\nwe exploit the ensemble library by deploying a Random Forest model as a\nmeta-learner. The ensemble method with random forest as meta-learner shows an\naccuracy of 91.8% which surpasses the other three ensemble combination methods,\nas well as other comparable models reported in the literature. The "majority\nvoting" and "optimal weights" combination methods result in prediction accuracy\nrates around 89%, while "average voting" is able to achieve an accuracy of only\n85%.\n
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".