Modeling Injury Severity of Unconventional Vehicle Occupants: Hybrid of Latent Segments and Random Parameters Logit Models
Bibliographic record
Abstract
Road safety research in developing countries has evolved in two categories: (a) crash frequency prediction modeling and (b) injury severity analysis. In injury severity analysis, the focus is to identify the influential factors for different injury severity categories. However, limited research has been undertaken in this domain, especially to assess the injury severity of the occupants of unconventional vehicles (UVOs) (including both human-pulled and engine-operated vehicles). This study investigates the injury severity of UVOs in Dhaka, Bangladesh adopting a hybrid of latent segments and random parameters logit (LSRPL) models. The model is developed utilizing police-reported collision records for the years 2011–2015. The LSRPL model captures multi-dimensional heterogeneity by allocating victims into discrete latent segments (i.e., inter-segment heterogeneity) and allowing a continuous distribution of parameters within the segments (i.e., intra-segment heterogeneity). The model is estimated for two segments using victim and crash attributes, where segment one is lower risk and segment two is higher risk. The model results suggest that victim and driver profiles, crash attributes, environmental factors, road network attributes, transportation infrastructure, and land use attributes influence the injury severity of UVOs. The model confirms the existence of significant inter-segment heterogeneity. For example, mid-block crashes are more likely to result in severe injury in higher-risk segments, and less likely to result in severe injury in lower-risk segments. The model further confirms intra-segment heterogeneity for areas with higher levels of mixed land use. For example, for mid-block crashes, higher mixed land use shows a significantly lower mean in high-risk segments, revealing lower likelihood of sustaining severe injury.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".