Partially constrained temporal modelling injury severity of crashes caused by risk-taking behaviours
Bibliographic record
Abstract
To investigate the factors influencing the severity of injuries resulting from risk-taking behaviors, comprehensive crash data from Rawalpindi City spanning 2017-2021 was meticulously analyzed. This study concentrated on three potential injury severity outcomes: property damage only, injury, and fatality. It examined a comprehensive range of variables encompassing driver, vehicle, roadway, environmental, temporal, and crash characteristics. Random parameter logit models with heterogeneity in means and variances were employed to effectively address the unobserved heterogeneity, exploring potential relationships between variables and random parameters. The presence of temporal instability was confirmed through likelihood ratio tests and out-of-sample predictions, and marginal effects were calculated to further elucidate this issue. Moreover, the partially constrained temporal modeling approaches were also developed and compared to the temporally unconstrained approaches. These findings not only contribute to the understanding of the nexus between risk-taking behaviors and injury outcomes but also shed light on the impact of the COVID-19 pandemic on traffic safety.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".