Improving Border Crossing Planning and Decision-Making in Whatcom County, Washington, and British Columbia, Canada
Bibliographic record
Abstract
In 2000, the Washington Department of Transportation (WSDOT) deployed the Whatcom County region’s first border wait time system, designed to address large vehicle wait time inconsistencies between the Peace Arch/Douglas and Pacific Highway Ports-of-Entry, situated less than one mile apart. Whatcom County, located in northwestern Washington State, borders the Canadian province of British Columbia (BC). In 2018, there were over 1.1 million commercial vehicles crossing the border between Whatcom County and the Lower Mainland of BC. Freight movement is essential for economic development and involves high levels of trade across international borders. However, when there are heavy truck and car volumes moving through these border crossings, there can be high congestion levels. Whatcom Council of Governments (WCOG) is the metropolitan planning organization for Whatcom County. Along with State, regional, and Canadian provincial partners, WCOG identified a need to improve the border wait times collection and reporting to inform travelers’ decision-making, to ease congestion, and to support the region in more effective transportation planning and management. As these initiatives evolved, there have been ongoing opportunities to assess border congestion impacts on freight mobility. Implementation of border wait time systems in Whatcom County and BC demonstrate how data-sharing across borders can improve transportation coordination, planning, and decision-making, including for freight. WCOG plays a critical role helping to compile and share wait time information across the U.S./Canada border. WCOG is the lead agency for the International Mobility and Trade Corridor Program (IMTC), a binational coalition of public agencies supporting border crossing systems in the region. WCOG’s members include seven cities in Whatcom County, the Port of Bellingham, and other regional entities. Although most border crossing technology implementation and management is under the IMTC purview, WCOG supports coordination and outreach to assist IMTC in implementing these initiatives.\n
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".