Measuring and Documenting Truck Activity Times at International Border Crossings
Bibliographic record
Abstract
Documenting the times trucks incur when crossing an international border facility is valuable both to the private freight industry and to gateway facility operators and planners. Members of the project team previously developed and implemented an approach to document truck activity times associated with an international border crossing by using technologies that are already in use by truck fleets. The approach relies on position, navigation, and timing (PNT) systems in the form of on-board global positioning system (GPS)-enabled data units, virtual perimeters called geo-fences that surround areas of interest, and a mechanism for data transmission. The investigators teamed with a major North American freight hauler whose trucks regularly traverse two of the busiest North American freight border crossings – the privately owned Ambassador Bridge, connecting Detroit, Michigan, and Windsor, Ontario, and the publicly owned Blue Water Bridge, connecting Port Huron, MI, and Sarnia, ON – to determine times associated with the multiple activities associated with using the facilities at these border crossing sites. Data were collected from the fleet over several months and processed to produce distributions of overall crossing times, queuing times, and inspection times for U.S.-bound and Canada-bound trucks. Parallel to these efforts, Transport Canada (TC) and the Ontario Ministry of Transportation were using a Bluetooth-based approach to collect truck data at these major border crossing facilities. In this study, the geo-fence approach and the data collection and processing efforts are described. Changes in roadway infrastructure at the border crossing facilities that could affect results obtained with presently implemented geo-fences are also summarized. Empirical comparisons are conducted between truck volumes and crossing times in the geo-fence and Transport Canada datasets. In addition, interest in the type of results produced from the geo-fence approach expressed by individuals associated with border crossing times is summarized.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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".