Enhancing dynamic resilience of traffic network: Role of Bayesian pre-posterior analysis supported by real-time congestion scanning technology
Bibliographic record
Abstract
ABSTRACT Major unanticipated disruptions to the traffic network that result in severe delays to travellers and adverse socio-economic impacts require an emergency preparedness strategy. Examples are bridge collapse caused by collision with a ship or fire or structural failure, and bridge closure due to stormwater. Although the inherent resilience of the traffic network can reduce the intensity of effects, dynamic resilience is required to further reduce delays until the disruption is removed or necessary demand management actions are implemented. The emergency preparedness mandate can be well-served with dynamic resilience action tested in a digital twin of the traffic network. This paper reports research on the development of dynamic resilience capability of the traffic network. Following problem definition, the methodological framework is described that incorporates dynamic stochastic assignment method-based traffic control system, real-time congestion scanning technology, and Bayesian pre-posterior analysis method that requires the use of scanning technology. The developed methodological framework guides decisions on the choice of dynamic resilience action and implementation time to minimize delay. The Chaudière Bridge that links Cities of Ottawa and Hull/Gatineau in the Canadian National Capital Area is used as a case study. This bridge outage caused by stormwater demonstrates the role of dynamic resilience in addressing uncertain states of post-disruption traffic delay. As compared to the business-as-usual traffic control, the dynamic resilience action-based control reduces delay by 12.3% under very high delay condition. The developed new methodological framework for analyzing network resilience can be applied to other major network disruption cases to reduce delay.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".