Managing disruption of rail-truck hazmat networks: a machine learning–optimization approach
Bibliographic record
Abstract
Rail-truck intermodal networks serve as major freight infrastructure, transporting both regular and hazardous material. Accidents and infrastructure failures pose a significant threat to these networks due to associated losses to life, the environment, and the economy. Dealing with these risks is challenging due to the physical and economic scale of the problem. Developing efficient disaster management plans is thus operationally and economically quite challenging. We propose an optimization and machine learning methodology for this problem. In this methodology, impact-based categorization and classification of unknown service legs or intermodal terminals are done via appropriate clustering and classification models, while for the optimization of the shipment plans, a bi-objective model is developed that employs network criticality measures as determined in the machine learning phase. The methodology was applied to a rail-truck intermodal network in the United States. The results indicate that post-disruption consideration should be incorporated into the transportation planning problem; machine learning algorithms can efficiently categorize network elements with high accuracy; and efficient pro-active post-disruption management can avoid a significant increase in cost and associated risks.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".