A Latent Class Modeling Approach for Identifying Injury Severity Factors and Individuals at High Risk of Death at Highway-Railway Crossings
Bibliographic record
Abstract
The growing focus on improving railway freight transportation in North America has resulted in increased attention to safety at highway-railway crossings (HRC). Recently, federal government agencies such as US Federal Railroad Administration (FRA) and Canadian Transportation Safety Board (TSB) have alluded to safety concerns associated with HRC. Safety at HRCs are of considerable importance to the government as well as the public due to the significant economic and emotional damages associated with accidents at HRC. To address these safety concerns, transportation researchers are focusing on developing countermeasures that enhance safety at HRC. Earlier research on HRC safety has employed a risk based approach considering both frequency and consequence; however, there has been very little research examining the consequence of the collision. In this paper, we aim to identify the different factors that influence injury severity of highway vehicle occupants, in particular drivers, involved in a vehicle-train collision. The commonly used approach to modeling vehicle occupant injury severity is the traditional ordered response model. However, the ordered response model restricts the effect of various factors on injury severity to be constant across all accidents. It is possible that accidents might be grouped (clustered) into different segments to differentiate the effects of various factors at the segment level. The current research effort proposes an innovative latent segmentation-based ordered response model to study injury severity. In this case, individuals (drivers) are assigned probabilistically to different segments with probability of getting injured specific to each segment. The validity and strength of the formulated collision consequence model is tested using the United States Federal Railroad Administration database which includes inventory data of all the railroad crossings in the US and collision data at these HRC crossings from 1997 to 2006. The research effort will shed light on the most important factors that affect the severity of injuries to vehicle occupants involved in collisions.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".