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Record W3123411670

Projections of Washington-British Columbia Trade and Traffic by Commodity, Route and Border Crossings

2007· preprint· en· W3123411670 on OpenAlexaboutno aff
Hamiltion Galloway, Ken Casavant, Eric Jessup

Bibliographic record

VenueRePEc: Research Papers in Economics · 2007
Typepreprint
Languageen
FieldBusiness, Management and Accounting
TopicTransportation Systems and Infrastructure
Canadian institutionsnot available
Fundersnot available
KeywordsCommodityOrder (exchange)TruckBusinessFrontierInternational tradeTransport engineeringEconomyFinanceGeographyEngineeringEconomics
DOInot available

Abstract

fetched live from OpenAlex

Continuing adaptation to the changing transportation needs of the U.S. and Canada is critical in maintaining efficiency and reducing costs of raw and manufactured goods. As NAFTA moves into its twelfth year of existence, there is a growing need to continue adapting to the changing transportation environment. With bilateral trade in excess of $1.2 billion per day between the U.S. and Canada and over 200 million annual crossings (passenger vehicles and freight trucks), knowledge of the composition of commodities crossing the border allow for easier adjustment to and support for the changing needs of industries and transportation providers. Since Washington borders Canada and acts as an international trade hub for the state as well as industries throughout the United States, there is a specific need to evaluate the composition of commodities at its key border ports in order to project future traffic. This project identifies key commodity groups in order to create a profile of major and minor Washington border ports in order to develop traffic projections. The central resource used to create the profile is the Strategic Freight Transportation Analysis (SFTA) database, a compilation of freight origin-destination survey results. The survey, not known to be duplicated by any other state, allows for the decomposition of freight flows by commodity, both northbound and southbound, thus allowing profiles to be created for seven major and minor border ports in Washington. The border ports analyzed are: Blaine/Pacific Highway, Lynden/Aldergrove, Sumas/Huntington, Oroville/Osoyoos, Danville/Carson, Laurier/Cascade, and Frontier/Patterson. Furthermore, SFTA allows for the decomposition of routes, which are used to estimate the flow of freight traffic on major Washington arterials, providing a profile of arterial highway usage by each border port. Once the profile was created, projections of northbound and southbound crossings from 2006 to the year 2015 were estimated for each border port. Linear regression trend line analysis was used to determine the potential growth of crossings based on the growth of trade between the U.S. and Canada. After projected crossings were initially estimated, projections of future northbound and southbound trade by Harmonized System of Commodity Classification Codes (HS) at the 2-digit commodity level, as well as projections of U.S. and Canadian industries, were combined with SFTA to determine the future composition of commodities crossing through the various border ports. These projections of traffic based on trade were then compared with the initial border crossing projections. The process used to determine the projections is shown in Figure 1. The top seven 3-digit NAICS commodity categories crossing the various Washington border ports are: food products, chemical products, plastic & rubber products, wood products, paper products, metallic products (fabricated and primary), and non-metallic mineral products. The NAICS categories were then translated into HS categories for 2 projections. The findings are in part corroborated with the Harmonized System trading commodities between British Columbia and Washington, as well as between Washington and Canada. The truck crossing findings show that the percentage growth in the number of northbound and southbound crossings by border port, based on 10-year average annual percent changes, range from -6.1% to 3.82%. The 10-year average annual increases for bidirectional trade range from 0.81% to 4.7%. As trade growth averages change over time, so will the commodity profiles of the specific border ports. When truck crossings are incorporated with trade growth we see a difference of 0.62% and 15.46% between the original “naïve” truck crossing projections and the new trade adjusted truck crossing projections. These projections on the future traffic volume and composition of commodities crossing between Washington and British Columbia serve as a guideline for future transportation of traded goods and the infrastructure investments necessary to support those flows.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.005
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.016
GPT teacher head0.277
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2007
Admission routes1
Has abstractyes

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