A Subsidy Policy to Ensuring Risk-Equity in Railroad Hazmat Transportation Network: A Risk Mitigation Strategy
Bibliographic record
Abstract
Railroad is one of the primary modes for transporting hazardous materials (hazmat). Given the dangerous nature of the hazmat, risk mitigation in the railroad transportation is the need of the hour. Hence, we explore the idea of equitable distribution of risk in the railroad network. We propose the subsidy policy to be considered by government to induce favourable routings of the hazmat shipments. The government's objective is to achieve risk equity in the network, whereas, the carrier's cost effective approach leads to increased risk in low-cost service-legs. To model this, we formulate the problem as a bi-level mixed integer program. We derive the single level mixed integer linear program (MILP) and test it on the rail infrastructure in midwest United States using state-of-the-art solver CPLEX 12.8.0. The instances with upto 25 shipments on the network are solved efficiently on a local machine. We use high performance computing resource available at Graham cluster of Compute Canada facility to solve the large instances with 50 shipments on the network. We show the effectiveness of the subsidy policy as a risk mitigation tool for the railroad hazmat transportation, and review the efficiency of the solution methodology to solve the MILP for the network. Moreover, the results demonstrate the economic feasibility for the government to allocate the budget for the subsidy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".