Managing disruption of rail-truck hazmat networks: a machine learning–optimization approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 it