Comparison of GPS and Driver-Reported Urban Commercial Vehicle Tours and Stops
Bibliographic record
Abstract
The objective of this paper is to compare two methods for commercial vehicle tour-based data collection, a paper-and pencil questionnaire and a GPS augmented paper-and-pencil questionnaire. Comparison of stop identification, tour attributes, and dwell-time are assessed in detail to show the potential for GPS data to add accuracy and precision, but also to show limitations of both methods. The data are from the Region of Peel Commercial Travel Survey, a combined shipper-driver survey that was recently conducted by the University of Toronto in the Region of Peel. Implementation of the survey indicated that recruiting randomly selected drivers to undertake a paper-pencil survey with a GPS supplement is difficult; however, after the driver is recruited they are far more likely to follow through with the survey than those without a GPS supplement. The results of the survey show that care must be taken in the use of GPS to select an appropriate threshold dwell time duration for stop identification. Significant under-reporting of stops was found in paper-pencil surveys, due to unfinished (truncated) surveys, missing stops throughout the day, and inaccurate stop location information. Such missing stops were found to cause misidentification of tours. Stop durations, however, were reported accurately by commercial vehicle drivers.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".