Identification and prioritization of hazardous material transportation strategies using DEA method
Bibliographic record
Abstract
Development of industries needs expansion of public transportation and, consequently, heavy transportation increases hazardous and dangerous transportation too.Therefore, we need to consider some strategies to reduce bad effects of transportation of hazardous materials such as road accidents.Strength, Weakness, Opportunity and Treats (SWOT) analysis is an applicable method for designing strategies in this area.However, SWOT analysis does not provide specific strategies and it does not consider the efficiency and performance of each strategy.Applying a hybrid method of analyzing the strategies and their performance evaluation help decision makers select the best strategies based on the current limitations.In this paper, different strategies for hazardous transportation risk reduction are designed and relative efficiencies of all alternatives are compared using DEA method.The proposed model of this paper uses three inputs including implementation costs, operation and maintenance cost and operational and four outputs including accident rate reduction, fuel consumption reduction, employment rate increment and deaths number reduction.The results of the implementation using seven different strategies have yielded two important strategies including continuous improvement of vehicle standards, driving skills, transportation system quality, and loading methods and expansion of petroleum pipe 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".