Temporal and out-of-sample prediction analysis of motorcyclists injury severities
Bibliographic record
Abstract
Motorcycle crashes represent one of the most severe threats to road safety, with disproportionately high rates of fatalities and injuries compared to other road users. This study investigates the factors influencing motorcyclists' injury severities in single-vehicle (SV) and multi-vehicle (MV) crashes, using the United Kingdom motorcycle crash data from 2016 to 2020. Motorcyclist injuries are categorized into minor, severe, and fatal. A random parameters multinomial logit model with a heterogeneity approach in means and variances is applied to model injury severities, addressing multiple layers of unobserved heterogeneities. To assess the temporal instability of significant factors, a series of likelihood ratio tests is conducted. The findings reveal transferability between SV and MV crashes, with significant temporal instability over the five years. The findings reveal substantial differences in determinants of SV and MV crashes: for example, motorcyclists aged 25-55 years involved in SV crashes had a 0.0292 higher probability of fatal injury, while elderly non-motorcycle drivers (over 65 years) significantly increased motorcyclists' likelihood of sustaining fatal injuries in MV crashes. The out-of-sample prediction simulation highlights substantial differences in injury severity probabilities across accident types (SV and MV) and over time. This research underscores the importance of considering SV and MV crash transferability and temporal instability to capture unobserved effects influencing motorcyclist injury severity. The statistically significant variances between SV and MV crash injury severity models offer insights for distinct policy interventions targeting SV and MV motorcycle rider safety.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.003 | 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".