Modeling Vehicle Collision Injury Severity Involving Distracted Driving: Assessing the Effects of Land Use and Built Environment
Bibliographic record
Abstract
This paper presents the findings of the vehicle occupant injury severity model, particularly focusing on the collisions involving distracted driving. The study develops a latent segmentation-based logit model for analyzing crash injury severity utilizing police-reported collision data from 2007 to 2011 in Nova Scotia, Canada. A segment allocation model is estimated to capture latent heterogeneity based on individual victims’ and drivers’ profiles, and collision attributes including vehicle type, vehicle trajectory, collision object, and collision type. The segment allocation model results suggest the existence of high-risk and low-risk injury severity segments. This study extensively tests the effects of built environment characteristics. The model results suggest that rain, curved road, freeway, and mid-block collisions aggravate vehicle occupant injury severity; whereas, higher land use mix, longer length of sidewalk, and higher population density mitigate injury severity. Significant heterogeneity is found across the high- and low-risk segments. For instance, straight road alignment is found to yield higher injury severity in the high-risk segment and lower severity in the low-risk segment. Moreover, the model unveils the interplay between built environment and distraction type. Driver distraction by communication device increases injury severity at a curved road intersection. Additionally, distraction because of inattentiveness increases injury severity. The findings of this study assist road safety engineers and planners to identify effective countermeasures and awareness programs for reducing the crash injury severity or consequences for vehicle occupants.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".