Aggregate Truck Queuing Time Relations at the Ambassador Bridge and Blue Water Bridge Border Crossing Facilities
Bibliographic record
Abstract
Relationships between truck queuing times immediately upstream of primary inspection stations and truck volumes and inspection times are estimated at the Ambassador Bridge and Blue Water Bridge international crossing facilities. The estimations are possible because of the recent availability of queuing and inspection time data obtained through the deployment of new technologies. Since truck volumes are available only at the monthly level, truck-level queuing and inspection time data are converted to monthly values, and aggregate relationships are estimated. Relationships are estimated for each crossing facility when using a set of 54 months of data and when dividing the data into subsets that represent a past and a recent period. Despite the aggregate nature of the data, strong relationships are produced that exhibit increased queuing times with increased monthly truck volumes and queuing time-to-volume elasticities greater than one. Differences in estimated elasticities, depending on crossing facility and direction, are consistent with different roadway characteristics upstream of the inspection stations. Relations exhibiting increased queuing times with increasing inspection times are produced, and the estimated coefficients are large enough to reflect the impacts on queuing times of differences in United States and Canadian inspection times. Changes in estimated relations from past to recent periods are consistent with infrastructure projects and improvements at the crossing facilities.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".