Identifying Achilles-heel roads in real-sized networks
Bibliographic record
Abstract
Ensuring a minimum operational level of road networks in the presence of unexpected incidents is becoming a hot subject in academic circles as well as industry. To this end, it is important to understand the degree to which each single element of the network contributes to the operation and performance of a network. In other words, a road can become an “Achilles-heel” for the entire network if it is closed due to a simple incident. Such insight of the detrimental loss of the closure of the roads would help us to be more vigilant and prepared. In this study, we develop an index dubbed as Achilles-heel index to quantify detrimental loss of the closure of the respective roads. More precisely, the Achilles-heel index indicates how many drivers are affected by the closure of the respective roads (the number of affected drivers is also called travel demand coverage). To this end, roads with maximum travel demand coverage are sorted as the most critical ones, for which a method—known as “link analysis”—is adopted. In an iterative process, first, a road with highest traffic volume is first labeled as “target link,” and second, a portion of travel demand which is captured by the target link is excluded from travel demand. For the next iteration, the trimmed travel demand is then assigned to the network where all links including the target links run on the initial travel times. The process carries on until all links are labeled. The proposed methodology is applied to a large-sized network of Winnipeg, Canada. The results shed light on also bottleneck points of the network which may warrant provision of additional capacity or parallel roads.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".