Development of a trip reconstruction tool for GPS-based personal travel surveys
Bibliographic record
Abstract
To collect travel survey data for transportation models more precisely and conveniently, Global Positioning System (GPS) has been used as an assistant tool in some recent surveys. However, the current applications still involve substantial manual data processing. This calls for new approaches that derive essential travel data through automated processing of GPS data. The purpose of this research is to develop a trip reconstruction tool, identifying used links and modes, for GPS-based personal trip surveys. The former is based on a conventional map-matching algorithm, and the latter is a rule-based algorithm using features of 4 modes (walking, bicycle, bus and passenger-car). The developed methodologies were evaluated with GPS travel data and transportation networks of downtown Toronto. Compared with records of a respondent, results of the tool show that 78.5% of all traveled links were detected, and 91.7% of modes of all trips were classified correctly.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".