Investigation of Commercial Vehicle Crossing Times at Pacific Highway Port-of-Entry
Bibliographic record
Abstract
At the Pacific Highway port-of-entry between the United States and Canada, typical delays are known to regional carriers and internalized into schedules. The largest 5% of crossing times are not internalized into schedules, and cause significant disruption to regional supply chains when they occur. In this paper, the authors describe the pattern of very long delays (defined as more than 2 hours or the largest 1% of crossing times), and explore whether the delays are caused by large demand for border crossings, a small supply of border crossing capacity, or some combination of these two effects. This understanding is necessary both to inform the discussion of border delay and to inform policy solutions. To do so the authors use commercial vehicle crossing time data obtained from a private fleet and commercial vehicle volume data from the British Columbia Ministry of Transportation. The authors analyze the patterns of delay and arrival on various temporal scales including monthly, seasonal, hourly, and day of the week. The authors discover a surprisingly weak correlation between border crossing time and arrival volume. The authors also discover a surprisingly high percentage can be attributed to sources other than primary booth delay, such as immigration, secondary screening, and a lack of preparation. This leads to the conclusion that solutions to border delay at Pacific Highway should not focus solely on increased primary inspection booths, but also on programs to increase border preparedness and trusted traveler programs.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".