Development of a daily updated train derailment impact map and analytics system for the national railway network in Canada
Bibliographic record
Abstract
A systematic analysis of the past train derailments and their impact can help industry and regulators to identify potential safety gaps so as to develop an effective method to mitigate the future risk. There exists a large body of data in the public domain to support such analysis, including the historical derailment records, geography, environment, demography and weather conditions along the railway network. The challenge is how to integrate and make use of the data for improving rail safety in Canada. This paper describes a daily updated Train Derailment Impact Map and Analytics (TDIMA) tool recently developed. A multidimensional database was first developed to integrate data from multiple data sources. Besides the historical number of derailments, the potential consequence was integrated as an additional element in the tool. This is the key difference between the developed tool and the existing ones that use number of accidents as the main variable to trend the risk. Multiple smart interfaces were designed to facilitate visualization-style analytics. With a few clicks, the tool can be used to perform analytics effectively. Case studies show that the trend of rail safety obtained only by number of past derailments can be different from that obtained by using the impact defined as the product of derailments and consequence. Ranking the cause of derailments by the defined impact resulted in different top causes than that ranked by only using number of derailed cars. This result provides the industry and regulator a different perspective to review the maintenance priority. Case studies on crossing, fire, and broken rail / wheel accidents are also discussed. It is also demonstrated how the tool can be used to review and monitor the latest rail accidents on an interactive map.
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.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".