Investment and policy decisions involving rural road networks in Saskatchewan : a network design approach
Bibliographic record
Abstract
Worldwide, rural road networks serve a vital link in the chain leading goods to markets and people to places. The efficiency of rural road network services is influenced by road-related investment and policy decisions. Reaching good decisions, however, is complicated by: interrelationships among policy, investment, road use, road performance, and rural economies; and combinatorial challenges involving the distribution of discrete policy and investment arrangements across networks.The main objective of this study is to address this complex problem as it pertains to rural road networks in Saskatchewan. Rural roads in Saskatchewan are suffering under increasing volumes of heavy truck traffic motivated principally by recent changes in the grain handling and transportation system. To address this problem, Saskatchewan Department of Highways and Transportation is considering a range of haul policy and road structure investment options. The question is, what (spatial) arrangement of available policy and investment options best meets this challenge. To answer this question, a cost-based standard is incorporated within a network design modeling approach and solved using custom algorithmic strategies. Applied to a case study network, the model determines a demonstrably good arrangement of costly road structure modifications under each considered policy option. Resulting policy-investment combinations are subsequently ranked according to total cost and equivalent net benefit standards. A number of important findings emerge from this analysis. Policy and investment decisions are linked; spatial arrangement of road structure modifications is contingent on the haul policy regime in place. Road performance and use characteristics are indeed sensitive to policy and investment decisions. Optimal budget levels computed by the model contradict perceptions that rural road networks in Saskatchewan are grossly under-funded. Despite best intentions, ill-considered policy can actually reduce the net benefits of road provision and use. Model application and design limitations suggest promising avenues for future research. These include: model larger networks in Saskatchewan and beyond; determine optimal road budgets under benefit-cost standards reflecting competing economic needs; employ model within regional economic planning investigations to forecast road-related implications; and model policy endogenously to aid design of heavy haul sub-networks and to address questions concerning network expansion or contraction.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".