Modeling Bicyclists’ Injury Severity Levels in the Province of Nova Scotia, Canada, Using a Generalized Ordered Probit Structure
Bibliographic record
Abstract
This paper examines the factors affecting injury severity in bicycle collisions using a generalized ordered probit model. One of the unique features of the modeling approach adopted in this paper is its flexible threshold structure, which incorporates individual variations in the thresholds to account for heterogeneity likely present in the data, but not commonly accommodated in traditional ordered probit models. Additionally, previous research has focused primarily on the factors that affect injury severity for motorists; generally, less attention has been given to understanding factors that affect injury severity levels for cyclists. Furthermore, examination of neighborhood and land use attributes in association with injury severity is surprisingly limited in the existing literature. This study attempts to fill the gap, particularly in understanding how land use and neighborhood characteristics affect injury severity levels for bicyclists. The data covers 2007-2011 bicycle collisions taken from police collision reports from the Province of Nova Scotia, supplemented with Census tabulations, provincial land use information, and point of interest data specific to the individual collision locations. The results reveal that females, impaired cyclists, and persons aged 45-54 involved in bicycle collisions have an increased likelihood of sustaining more severe injuries. Road condition and configuration, bicyclists’ manoeuver, and lighting conditions also affect cyclists’ injury severity levels. Finally, characteristics of the neighborhood in which collisions occur, often ignored in previous collision studies, for instance land use mix, proximity to activity centers, and demographic attributes are found to be significant in explaining injury severity of bicyclists. The results suggest that neighborhood characteristics should be given more scrutiny and be an important consideration when evaluating and planning for cyclist 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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".