Evaluating the impacts of infrastructure improvements based on link criticality and network performance: A case study of the trucking industry in the province of Ontario, Canada
Bibliographic record
Abstract
Abstract A reliable transportation network is essential to maintaining and growing a region's economic productivity through the movement of goods, a significant proportion of which is moved by truck. Criticalities in the network should be identified and mitigation measures implemented to ensure that minimal negative impacts arise from link disruptions. Using criticality measures that account for network, freight flow, and economic characteristics, a comparison is made among locations of highly critical segments in the province of Ontario, Canada, and infrastructure improvement projects planned by the Ministry of Transportation of Ontario. Four highway capacity expansion segments are explored through a scenario analysis, comparing the effects resulting from their implementation to a status quo base case. Freight and passenger flows are forecasted to the year 2036 for the analysis. A comparison is made among the scenarios to assess the network‐wide impacts of each segment's improvements with respect to vehicle and shipment value flows, travel time, greenhouse gas emissions, and each segment's average operating conditions. Of the four segments compared, the improvements of Highway 404 appear to provide the most significant benefits with respect to network performance. Such analysis can inform policy measures for the prioritization of infrastructure improvements to address network criticalities.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".