Comparing GPS and Non-GPS Survey Methods for Collecting Urban Goods and Service Movements
Bibliographic record
Abstract
This paper describes results of the Region of Peel Commercial Vehicle Survey, a pilot data collection effort that collected commodity, mode choice, and commercial vehicle movement data from a sample of approximately 600 shippers and a sample of their drivers, in the Region of Peel, located just west of the City of Toronto, Canada. Two survey techniques are tested, including a mail-out/ mail-back survey and a mail-out/ mail-back survey with a GPS-supplement. This paper describes the survey method, the results and the methodological lessons learned in the urban goods movement study. A comparison of survey implementation results for the two types of surveys are provided, including overall survey response rates and item non-response. Analysis of the quality of shipper, driver and GPS portions of the data are outlined with checks for consistency. Comparisons of commercial vehicle tour behavior and stop location and time as reported in the paper survey forms and as recorded in the GPS units are made for an evaluation of the effectiveness of the GPS in identifying stops, and the potential of GPS as a passive replacement for more traditional paper and pencil survey methods.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".