Improving Mobility at the U.S./Canada Border through Border Wait Time Data-Sharing: the Buffalo-Niagara Falls Region
Bibliographic record
Abstract
On an annual basis, over 1.1 million trucks and more than 5 million vehicles cross the Peace Bridge that spans the Niagara River and connects Western New York State and Southern Ontario, Canada.1 Twenty- five miles north on the Niagara River, about 800,000 trucks and 2.6 million vehicles annually cross the Lewiston- Queenston Bridge.2 Trucks crossing the U.S./Canada border play critical international trade and economic development support roles. However, heavy truck and car volumes at the U.S./ Canada bridge crossings can create high congestion levels with significant impacts. In 2012, the region’s first border wait time technologies were deployed on the Peace Bridge and Lewiston- Queenston Bridges, with funding support from Transport Canada and the Federal Highway Administration (FHWA). Additional stakeholders who supported these deployments (through funds or other coordination) included the Buffalo and Fort Erie Bridge Public Authority, or Peace Bridge Authority (PBA), which manages the Peace Bridge; the Niagara Falls Bridge Commission (NFBC), which manages the Lewiston- Queenston Bridge, Rainbow Bridge, and Whirlpool Bridge; U.S. Customs and Border Protection (CBP); and the Canada Border Services Agency (CBSA). Travelers (including commercial vehicle operators) crossing the U.S./Canada border have an ongoing need for realtime border wait time information to improve trip planning and mobility. The information can also aid the public and private sector in more informed transportation planning, performance management, and decision-making. The Niagara International Transportation Technology Coalition (NITTEC) serves as the region’s traffic operations center that compiles, analyzes, and distributes real-time travel data to the public and other stakeholders. As part of its regional traffic mobility improvement mission, NITTEC provides a publicly-accessible online travel information clearinghouse that includes border wait time.\n
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".