Reliability-Based Estimation of Traffic Interruption Probability due to Road Waterlogging
Bibliographic record
Abstract
Floods affect road infrastructure physically and operationally, increase road user costs and road agencies cost, and eventually isolate communities. The research of the effect of floods on vehicular circulation is mainly focused on the stability of vehicles. There are few studies related to the regime of still water in the trafficability. In still water, the speed flow is low and does not compromise the vehicle stability. In this case, the vehicle’s wading height becomes relevant. This article proposes a procedure to estimate the traffic interruption probability caused by floods in roads, considering the still water regime. The procedure uses the first-order reliability method to estimate traffic interruption probabilities, based on the difference between the probability density functions (PDF) of still water depth (or waterlogging depth) and vehicle wading height. A specific procedure to estimate the PDF of wading height based in the geometric characteristics of light and heavy vehicles was developed. The PDF for water depth was estimated using water level profile simulation software. The procedure was applied in the south of Chile. The PDF of wading height was obtained from a record of 166,155 vehicles tagged in open road tolls. The PDF of waterlogging depth was obtained from discharge records of 10 fluviometric stations. 42 probability curves were obtained for six vehicle classes and return periods between 4 and 500 years. The still water depth obtained for traffic interruption probability of 1,0 varied between 70 and 90 cm for light vehicles and between 110 and 150 cm for heavy vehicles.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".