Designing Reliable Transport Networks: The Multiple Network Spoiler Approach
Bibliographic record
Abstract
Transport networks are expected to remain highly reliable to ensure that users can experience smooth travel under both normal and abnormal traffic conditions. To design a reliable transport network properly it is necessary to take into account users reactions to incidents which cause disruption in different parts of the network. In order to design a reliable network to deal with such events, it is necessary to capture the users route choice behavior under degraded conditions. Furthermore, for practical reasons, it is imperative that any improvement scheme should give priority to links in the network which are most vulnerable to failure. Therefore in this paper we formulate a reliable continuous network design problem (RCNDP) in which the link flow is assigned to different routes considering the risk-taking behavior of the network users when confronted by network uncertainty. To capture this behavior, we pose the RCNDP under the multiple network spoiler formulation of risk averse traffic assignment. The ability of this model to introduce higher capacity expansions on links vulnerable to failure and consider many operational states to cater for the worst-case scenarios of network reliability is demonstrated through two numerical studies.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".