Travel mode classification based on <scp>GNSS</scp> trajectories and open geospatial data
Bibliographic record
Abstract
Abstract Accurate travel modes inferred from global navigation satellite system (GNSS) trajectory data can be instrumental to city development, such as travel planning and modeling and traffic flow prediction. With the advancement of mobile sensors, the Internet, and GNSS devices, massive GNSS trajectories have been recorded, laying a foundation for travel mode classification at a fine granular scale. However, the lack of discriminative features reduces the accuracy and robustness of travel mode classification. Thus, in this study, considering the relationship between trajectories and the surrounding transportation environment, a set of discriminative features extracted from geospatial data combined with various GNSS features generated from GNSS trajectories is proposed to better distinguish different travel modes. Based on this, we conduct a systematic comparison of a group of state‐of‐the‐art methods using GeoLife and OpenStreetMap (OSM) data, the results of which will provide guidance for properly selecting models for future travel mode classification‐related work. In addition, the comparison results show that adding GIS‐based domain expert features is robust in improving the classification accuracy of all classifiers in this study.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".