Performance Measures for Road Networks: A Survey of Canadian Use
Bibliographic record
Abstract
Encompassing 1.4 million kilometres, the road network in Canada is vital to the country. Managing the road network is becoming increasingly challenging as demands increase and resources are limited. To face those challenges, performance measurement is attracting growing interest from transportation agencies. With the expectation that what is measured can be better managed; performance measurement is being implemented as a core component of management processes in public sector agencies. In Canada, most provinces and territories use some form of performance measures to evaluate their road networks. However, the type of performance measures used and the implementation practices vary significantly between jurisdictions. This project, conducted under the auspices of the Chief Engineers' Council of the Transportation Association of Canada, was intended to share experiences between jurisdictions on their performance measurement practices. The report provides an overview of the literature available on the subject. Reasons to measure performance within transportation departments are cited. Issues to consider when developing a performance measurement program are offered. It is observed there is not one measure, or one set of measures, that can be considered the best for all cases. In each case, the performance measures practice depends on the specific conditions of an agency, its goals, its resources, and its audience. The primary focus of the project was to survey Canadian provincial and territorial jurisdictions regarding current practices for performance measurement of road networks. The results of the survey are categorized in six outcomes: safety; transportation system preservation; sustainability and environmental quality; cost effectiveness; reliability; and mobility/accessibility. The survey revealed the following: transportation system preservation appears to be the most highly developed and mature application of performance measures in Canadian highway agencies; safety performance is a priority interest, with most agencies using accident rates per million vehicle kilometres as a key measure; outcomes of cost effectiveness, reliability, and mobility/accessibility are subject to performance measurement in some jurisdictions with little consistency in application; measures to assess performance on sustainability and environmental quality are used to a limited extent by Canadian agencies. The report also provides an international perspective on trends in performance measurement of road networks focusing on the United States, Europe and Australia. There is considerable commonality amongst the categories of performance measures that are used internationally. Austroads is cited as having the most ambitious and long-standing performance measurement program, with 72 national performance indicators in ten categories. For the covering abstract see ITRD E139491.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".