Segmented Ordered Logit Analysis of Gender and Bicycle-Vehicle Conflict Occurrence at Urban Intersections
Bibliographic record
Abstract
The relative lack of safety for cyclists in North America demands attention from transportation professionals. Traffic crash modelling is one tool for evaluating the factors that contribute to cyclist risk, though traditional safety models calibrated with crash data require crashes to occur before causes can be identified and countermeasures can be implemented. Although surrogate safety measures have diminished reliance on crash data, surrogate techniques have yet to be integrated with traffic crash models. The purpose of this study is to estimate a segmented ordered logit model for bicycle-vehicle conflict occurrence to evaluate the impact of gender on cyclist risk at urban intersections with cycle tracks. Video data was collected at two sites in Montreal, Canada. Road users were extracted, classified, and filtered using open-source computer vision software to yield 762 interactions for analysis. Creation of the discrete choice variable was achieved by dividing post-encroachment time (the chosen surrogate measure) into normal interaction, conflict, and dangerous conflict. Independent variables reflecting attributes of the cyclist, vehicle, and environment were extracted by both automated and manual techniques. Results indicated that an ordered model is appropriate for analyzing traffic conflicts. Furthermore, segmentation was beneficial in comparing different segments of the population within a single model. Male cyclists, with all else being equal, were less likely than female cyclists to be involved in conflicts and dangerous conflicts at the studied intersections. These results will contribute to and further the understanding of gender differences in cycling within North America.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".