Exploring an Infrastructure Investment Methodology to Risk Mitigation from Rail Hazardous Materials Shipments
Bibliographic record
Abstract
Abstract Railroad is one of the primary modes to transport hazardous materials (hazmat) in North America. For instance, Canadian railroads carried around 50 million tons of hazmat in 2018. Given the inherent danger of trains carrying hazmat, this study aimed at exploring a novel way towards mitigation of the associated risk. This study sought to investigate whether proper rail track infrastructure investment can mitigate the risk from hazmat shipments. To this end, a methodology was developed and then applied to the Canadian railroad network. The proposed three-step methodology captured the differing perspectives of rail carriers and regulatory agencies, and entailed (1) ascertaining the risk-level of various yards and links in the given railroad network, (2) specifying potential candidates for infrastructure investment, and (3) finding the optimum set of investment decisions. The proposed methodology was then applied to the Canadian railroad network to demonstrate that significant risk-reduction can be achieved by adding alternative rail-links around the riskiest locations (i.e. the network hot-spots), and also to show that risk-reduction function is non-linear with non-monotonous behavior. The study showed the possibility of significant hazmat risk reduction through alternative rail-links that could take traffic away from the network hot-spots. The methodology and the results from the Canadian case can be used by railroad companies and policy makers to estimate the value of potentially risk-reducing infrastructure investments.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".