Comparing GPS and Non-GPS Survey Methods for Collecting Urban Goods and Service Movements
Bibliographic record
Abstract
This article, from a special issue on freight transport, presents the results of a pilot data collection effort that collected commodity, mode choice, and commercial vehicle movement data. The Region of Peel Commercial Vehicle Survey collected data from a sample of 600 shippers and a sample of their drivers from a region located just west of Toronto, Canada. The authors tested two survey approaches: a mail-based survey and a mail-based survey with a GPS supplement. They report on the implications of their findings for urban goods movement. 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 used to evaluate the effectiveness of the GPS in identifying stops as well as the potential use of GPS as a passive replacement for more traditional paper and pencil survey methods. The authors conclude that reasonable response rates are achievable if careful attention is paid to all aspects of survey design, pretesting, multiphase recruiting, and followup telephone calls; neither GPS nor driver-reported surveys are able, on their own, to reflect the full extent of commercial vehicle travel; and GPS supplements are logistically difficult but possible to implement and they tend to increase the response rate.
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 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 | 0.069 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".