Assessing the Risks Associated with the Canadian Railway System Using a Safety Risk Model Approach
Bibliographic record
Abstract
Canada’s national rail network plays a vital role in moving goods and people, transferring $320 billion worth of goods and over 100 million passengers annually. Severe train occurrences are rare events. But they have the potential to cause fatalities and injuries, as well as environmental and property damage. Recent severe incidents, such as Burlington in 2012 and Lac-Mégantic in 2013, have shown that there is still a need for increased awareness and enhanced risk assessment. This work focuses on risk assessment on the Canadian railway system using the Safety Risk Model (SRM). The study applied a customized Canadian SRM (C-SRM) to two groups of hazardous events: main-track derailments and collisions with fatality and injury consequences, calibrated for data between 2007 and 2017. The model used Fault Tree Analysis (FTA) and Event Tree Analysis (ETA) to identify the risks of hazardous events. The individual risks of the hazardous events were then evaluated for three groups of people: passengers, employees, and members of the public (MOP). Finally, the effectiveness of introducing a new control measure, Enhanced Train Control (ETC), was assessed. The results of the study showed that the collective risk of main-track derailments is higher than main-track collisions. Moreover, the risk to MOP and employees form the most significant proportion of individual risk. Finally, risk reduction analysis of the ETC revealed that developing this system reduced the risk of main-track derailments and collisions. This new control measure thus has the potential to make Canadian railways safer.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 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".