A Bayesian Modeling Approach for Cyclist Injury Risk Analysis at Intersections and Corridors
Bibliographic record
Abstract
This study proposes a two-equation Bayesian modeling approach to simultaneously study cyclist injury occurrence and bicycle activity at signalized intersections as joint outcomes. This approach deals with the potential presence of endogeneity and unobserved heterogeneities and is used to identify contributing factors associated with both cyclist injuries and volumes. Its applicability in the identification of corridors at high-risk is also illustrated. An extensive inventory of a large sample of signalized intersections on the island of Montreal is used as the application environment. This inventory contains not only disaggregate vehicular traffic volumes and bicycle flows but also geometric design, traffic controls and built environment characteristics in the vicinity of the intersections. Among other results, this study identifies the importance of both bicycle and motor-vehicle flows on cyclist injury occurrence and further emphasizes the importance of turning motor-vehicle movements. It was also found that the presence of bus stops and total crosswalk width have a positive effect on cyclist injury occurrence whereas the presence of a raised median has a negative effect. The results also reveal that bicycle activity through intersections increases as employment, number of metro stations, land use mix, area of commercial land use type, length of bicycle facilities increase as well as in the presence of schools measured between 50 and 800 meters from the intersection. Also, intersections with three approaches are expected to have fewer cyclists than intersections with four approaches. Using Bayesian analysis, expected injuries and injury rates are computed to rank corridors. The authors found that corridors with high bicycle volumes, located mainly in the central neighborhoods of Montreal, have lower risk despite having a high number of cyclists riding along each day. Most importantly, since there are more cyclists, these corridors have a greater chance of ranking high therefore confirming the safety in numbers effect.
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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 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| 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".