Seasonal variation in North American level crossing crash rates is due to weather, not day length
Bibliographic record
Abstract
Collisions between road vehicles and trains at level (grade) crossings can be devastating. Injury and economic considerations make prevention efforts of significant interest to society at all levels, and raise important safety concerns. Improving our understanding of the nature and pattern of crashes at level crossings can help inform a variety of types of safety mitigation strategies, including public education, crossing equipment and vehicle design efforts. To this end, a database search of Canadian level crossing crashes for the 11-year period between 2007 and 2017 was conducted to confirm a previously identified seasonal variation in the frequency of level crossing crashes. To determine whether the observed winter increase in crashes was due primarily to winter reductions in light levels/day length or to other seasonal weather factors, a subsequent comparison of Canadian data to American Federal Railroad Administration (FRA) crash data was carried out. A separate inferential log-linear model analysis, using season, time of day and crossing protection type, was also used to explore the increase. As expected, the average rate of crossing collisions in Canada increased during winter months compared to non-winter months. While the seasonal pattern was evident in those U.S. states that experience significant changes in weather patterns (i.e., northern states), it was almost completely absent in those states that do not (i.e., southern states). The log-linear model analysis confirmed these findings. The seasonal variation in North American level crossing crash rate is a result of winter weather conditions, rather than differences in light levels. Future research is planned that will elucidate the specific environmental and human factors contributing to the increase.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".