New Brunswick Department of Transportation and Infrastructure's Strategic Multi-Criteria Analysis Model for Capital Infrastructure Investments
Bibliographic record
Abstract
This paper describes a multi-criteria analysis (MCA) model and methodology developed for the New Brunswick Department of Transportation and Infrastructure (NBDTI) for prioritizing new capital investments in its transportation infrastructure. The methodology and MCA model for transportation projects, including a user guide for its application, were developed by Opus International (Canada) Ltd. (Opus) in 2013. NBDTI has tested this MCA model & methodology and is currently considering utilizing it as a key component in its “Asset Management Decision Framework” to prioritize new capital transportation projects for its long-term strategic infrastructure plan. The department has recently initiated a continuous improvement project to extend the MCA model to prioritize other categories of infrastructure assets, specifically buildings. NBDTI has a mature highway asset management program to determine the rehabilitation needs of its existing transportation assets. However, the department required a defendable, transparent methodology for prioritizing capital investments in new infrastructure that: Enabled the Department, and in turn the province, to strategically prioritize and plan capital infrastructure expenditures on a more efficient and effective basis; Supported the province’s current commitment towards providing appropriate and affordable service to citizens on a sustainable basis; Aligned with the Government of New Brunswick’s vision and strategy map; and Better positioned the Department, and in turn the Province, to seek future cost shared funding from the Federal government. The paper describes the results of Opus’s three step approach to develop the MCA methodology and model including: 1. A literature and current practice review of North American agencies; 2. Stakeholder Consultations and the resulting draft methodology and model; and 3. Testing and refining of the draft methodology and model. The final model is presented along with examples of how it is being applied to: Assess the strategic value and expected performance of its planned capital infrastructure projects; Prioritize projects according to their strategic value; and Communicate the results. The paper concludes with recommendations on how the MCA methodology and model can be improved, and extended to other government assets and decisions on asset divestiture or disposal.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".