Effects of Hazmat Train Speed Restrictions on Train Delay Performance and Railroad Line Capacity: Comparative Study with Two Railway Simulation Tools
Bibliographic record
Abstract
In response to multiple derailments involving hazmat trains, in early February 2020 Transport Canada released ministerial order (MO) 20-02, imposing speed restrictions of 20 to 25 mph on trains transporting a sufficient quantity of hazardous material. Since much of the North American freight network is used by multiple train types, the extreme speed heterogeneity created by this mandate substantially reduced train performance. Although this order was replaced within 2 weeks by new speed restrictions that were in turn replaced in May, MO 20-02 introduced the most extreme levels of train speed heterogeneity. The research team investigated the corresponding capacity effects to better understand the effects of train speed heterogeneity at low speed and inform agencies on future speed restrictions in this range. Using Rail Traffic Controller and General Train Movement Simulator, we quantitatively investigated the capacity loss from these speed restrictions and found that MO 20-02 can double or triple average train delay and lead to mainline capacity loss in excess of 60% on a representative single-track mainline.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".