Assessing transportation network redundancy by integrating route diversity and spare capacity
Bibliographic record
Abstract
In this paper, we develop a new transportation network redundancy measure by integrating the route diversity and spare capacity dimensions, which have been demonstrated as two complementary dimensions of redundancy and assessed separately in the literature. Instead of modelling redundancy as two separate measures with significantly different units and scales, an integrative measure is beneficial for comprehensive comparison and selection of redundancy enhancement schemes. Specifically, we represent the transportation network by adding a virtual link directly connecting each origin-destination (O-D) pair. The virtual link choice probability is affected by both the route diversity of the O-D pair and the spare capacity of these routes, which is used in the proposed measure. Numerical examples in the realistic Winnipeg network are conducted. The effectiveness of the proposed measure is further verified by applying it to identify the critical links in the Shanghai Expressway Network, where a severe traffic accident happened in 2016.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.010 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".