Cascading Reliability of Multimodal Public Transit Networks With Higher Order Interactions
Bibliographic record
Abstract
Over time, significant progress has been made in planning and managing public transit networks (PTNs). However, most research developments are confined to pairwise interactions, thus offering restricted perspectives on the comprehension of higher order PTN reliability, particularly the exploration of cascading failure for multimodal PTNs (MPTNs). Hence, on the basis of the conventional coupled map lattice (CML) model, we propose a cliquey CML model in which failure propagation can occur via interactions within cliques of varying scales to investigate the higher order cascading reliability of MPTNs. In addition, models are constructed for three PTN types: bus-only, bus-metro, and bus-metro-taxi/ride-hailing networks. With Beijing MPTNs as empirical examples, we design various attack strategies to explore the resilience characteristics of MPTNs after being destroyed from different perspectives. Overall, Beijing MPTNs display favorable resilience and cascading reliability in the face of intentional attacks, and PTNs considering higher order interactions display better network stability than lower order node-line networks do because of the effect of cliques. Additionally, MPTNs generally exhibit better survivability than unimodal PTNs in scenarios with low perturbations and collapse relatively quickly in cases with high perturbations. This article provides a theoretical foundation for advancing research on the higher order dynamics of MPTNs.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".