Optimal Design of a Hazardous Materials Transportation Network considering Uncertainty in Accident Consequences
Bibliographic record
Abstract
Hazardous materials pose significant dangers during transportation due to their flammable and explosive properties. The consequences of accidents involving such materials are often severe and irreparable. A well‐designed hazardous materials transportation network can mitigate these risks. However, designing such a network presents two major challenges: quantifying the risk associated with hazardous materials transportation and addressing the hierarchical relationship between government and companies. To address these challenges, we enhance the accuracy of accident probability estimates and the comprehensiveness of accident consequence assessments, incorporating the uncertainty of accident outcomes. We propose a comprehensive risk assessment model and develop a bilevel programming model to reflect the hierarchical relationship. In this model, the government at the upper level aims to minimize the total risk, while companies at the lower level seek to minimize their total costs. The model is transformed using chance‐constrained programming and solved using heuristic algorithms. We apply the model to the highway network in Anhui province, China, to verify its validity. The results demonstrate that the model effectively manages the hierarchical relationship between government and companies, reduces the risk of hazardous materials transportation, and enhances the stability and safety of the transportation network.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".