Adaptive road border crossing system management: the role of priority programs
Bibliographic record
Abstract
SUMMARY Land border crossings in North America, such as those between Canada and U.S.A., are expected to experience severe imbalance of travel demand and capacity of processors. During peak travel periods, this is already the case at high traffic locations. The land border crossing authorities have to address problems of congestion, national security and environmental impacts in the operation of the existing systems and to continue to address these problems as a part of infrastructure expansion plans. There is a need to adapt the crossing system management in order to accommodate efficiency and productivity‐oriented priority crossing measures. From a methodological perspective, it is a challenge to evaluate the role of priority crossing measures within the complex border crossing system. This paper reports research on modelling priority crossing initiatives. A microsimulation approach was used to model and analyse integrated processors of the Peace Bridge crossing system between Fort Erie (Ontario) and Buffalo (New York) under different scenarios of travel demand, customs processing times, priority crossing and queue jump lanes for automobile and truck traffic. Findings show the extent to which a border crossing system with priority crossing and queue jump lanes is more efficient and productive than one without these innovations. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".