Costs of Urban Congestion in Canada
Bibliographic record
Abstract
The ability to address congestion requires, first, an understanding of the topic. To this end, different methods have been developed to quantify and compare congestion. A recent research study developed methods to quantify congestion and its costs in the nine largest urban areas in Canada. Three components of congestion and its costs—delay, wasted fuel, and greenhouse gas emissions—were developed. The methods were based on the travel demand—forecasting models of each urban area. In contrast to well-known methods such as the annual Urban Mobility Report in the United States, which uses common sets of data to allow a comparison of congestion in 85 urban areas, a comparative analysis was not possible because the models differ in structure, definition, and base data. Conversely, the model-based approach supports significantly more analytical depth, allows planners in individual urban areas to customize the tools, and provides a means to account for congestion in forecasts and in the development and evaluation of long-range transportation plans. It also provides a basis for linking the engineering aspects of congestion with the broader economic perspectives. The approach used in the Canadian study has potential for metropolitan planning organizations, state departments of transportation, and other U.S. transportation planning authorities that seek to incorporate the analysis of congestion into their long-range transportation planning, programming, and budgeting processes.
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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".