Impacts of Model Resolution on Transportation Network Criticality Rankings
Bibliographic record
Abstract
Objective rankings of the criticality of transportation network infrastructure are essential for efficiently allocating limited adaptation resources and must account for network connectivity and travel demand. Road link criticality can be quantified by the total travel delay caused when the capacity of a road segment or link is disrupted or removed. These methods can use standard travel demand models, but the exclusion of lower-volume roads and the aggregate nature of traffic analysis zones may distort resulting criticality rankings. To test the impact of link exclusion and demand aggregation, the authors applied the network robustness index, a well-established link criticality measure, to a hypothetical network with varying levels of network resolution and demand aggregation. The results show a statistically significant change in criticality rankings when demand is aggregated and especially when links are excluded from the network, suggesting that criticality rankings may be distorted when estimated with typical demand models. Application to a road network in Vermont supports the finding on the impact of network resolution on criticality rankings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".