Impact of Train Drivers’ Cognitive Responses on Rail Accidents
Bibliographic record
Abstract
Despite the innovations in automatic train control (ATC) systems to reduce the risk of driver error, many rail accidents still occur due to defects in these systems, emphasizing the essential role of the driver in preventing rail accidents and proper control of the train. This paper studies the influence of drivers’ cognitive performance, including attention and visual perception, on the occurrence of rail accidents. The research is conducted using so-called Ex-Post facto method on a random sample of 56 train drivers with a minimum of three years of experience. The research instruments included drivers’ cognition test system including WAFV (perception and attention function) sustained attention test, COG (cognitrone) selective attention test, LVT (visual pursuit) visual perception test, demographic questionnaire, and drivers’ safety history. Results of this research showed that there is no significant relationship between age and education level of train drivers, and rate of occurrence of rail accidents. A comparison on train drivers’ cognitive characteristics, between drivers with accident record(s) and those without, showed that the drivers who had experienced rail accident(s) had lower levels of sustained attention. However, no significant difference was found between the two groups in selective attention and visual perception. Investigating the association of drivers’ ages with their levels of sustained attention, the drivers with high levels of sustained attention were found to be significantly older than other drivers. According to practical implications of these findings, cognitive rehabilitation courses are recommended for train drivers to attenuate the risk of rail accidents.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".