Examining the Variability of Crossing Times for Canadian Trucks at the Three Major Canada–U.S. Border Crossings
Bibliographic record
Abstract
Land borders connecting Canada and the United States are vital transportation facilities for the two countries. Truck crossing times at these facilities can have a significant impact on the performance of the economy. To date, knowledge about border crossing times has been limited due to lack of detailed data on the Canadian border. This article explores and models the patterns of crossing times at the three major land crossings connecting Canada to the United States: Ambassador Bridge, Blue Water Bridge, and Peace Bridge. The analysis is based on 387,775 border crossing truck trips that were generated between Canada and the United States over a course of twelve months. Seemingly unrelated regression (SUR) models are estimated to evaluate the seasonal and hourly crossing times of Canada- and U.S.-bound trips on each border crossing, controlling for traffic intensity in the models. The SUR modeling approach is chosen to control for potential cross-model correlations. The results suggest that crossing times at the border vary by season and hour of the day. Crossing times also vary by direction of traffic and by type of day (i.e., weekday vs. weekend). Traffic intensity has a significant influence on crossing times at two of the crossings but not the Blue Water Bridge. Finally, crossing times are more variable during the summer season and tend to be higher during the late evening hours and past midnight.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".