Assessing the Impacts of Rainstorm and Flood Disaster for Improving the Resilience of Transportation System
Bibliographic record
Abstract
Rainstorm and the induced flood disaster often cause serious damage to transportation system, resulting in the loss of life and property. To improve the resilience of transportation system, this study proposes a framework for assessing the impacts of rainstorm and flood disaster on road network based on GPS data. The change ratio of traffic flow and congestion index are used to evaluate the disrupted and detour road sections caused by rainstorm. A method based on the change of traffic flow compared to the normal circumstances is proposed to identify the flood‐damaged road sections. A case study in Beijing verifies the feasibility and practicality of the proposed framework, which can effectively assess the impacts of rainstorm from multiple spatial and temporal dimensions, identify the flood‐damaged road sections, and supplement existing research. Research findings can provide scientific basis for the emergency management departments to accurately identify the roads affected by rainstorm and improve the resilience of transportation system.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".