Defining the Range of Urban Congestion Impacts on Freight and Their Consequences for Business Activity
Bibliographic record
Abstract
The causes and impacts of urban traffic congestion are intrinsically tied to changes occurring in business practices and the economy. The freight delivery requirements of businesses and their sensitivity to congestion are also increasing as many types of business seek to serve wider markets and apply new logistics and production technologies with increasing reliance on just-in-time supply chains, overnight courier services, intermodal facilities and international gateways. In response, regional business organizations are starting to take a leadership role in focusing attention on urban traffic congestion and its impacts on freight movement and business activity. This paper uses examples from three cases – Vancouver (BC), Chicago (IL) and Portland (OR) – to show how regional business organizations have been working with public agencies to study the economic implications of future congestion growth and the economic benefits of investing in efforts to mitigate it. It utilizes findings from those studies to develop a taxonomy of the many different ways in which urban traffic congestion is changing the freight delivery and operational decisions of businesses, and increasing their costs. It then identifies needs for improved transportation and economic analysis methods that are sensitive to those factors.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".