Vehice-Pedestrian Accidents at Signalized Intersections: Exposure Measures and Geometric Designs
Bibliographic record
Abstract
Pedestrian safety is a topic of growing concern. To better understand the environmental factors (traffic controls, geometric and land use factors) associated with pedestrian-vehicular accidents, this paper presents a methodology for quantifying pedestrian accidents at signalized intersections. For this purpose, a rich and unique intersection inventory with geometry and accident data was built and analyzed comprising a very large sample of 1,871 signalized intersections across the island of Montreal, Canada. To investigate the impact of vehicle movements, three separate definitions of risk exposure were used: completely aggregated flows, motor-vehicle flows aggregated by movement type (left, right and through movements) and disaggregated flows analyzing potential conflicts between motor vehicles and pedestrians. Various negative binomial models were fitted to the data with and without geometric design characteristics. Among other findings, vehicular traffic is found to be the main contributing factor in accordance with previous works. It was a bit surprising to see that through vehicular movements at intersections had a greater effect on accident rates than left and right turns. A separate analysis was conducted to incorporate geometric variables into the risk exposure models. Significant geometric properties included pedestrian phasing, exclusive left turn lanes, commercial entrances and exits, total crossing distance, curb extension and number of lanes. Exclusive left turn lanes, pedestrian phasing and curb extensions were found to decrease pedestrian accidents, whereas longer crossing distances, number of lanes and more commercial entrances and exits were found to significantly increase pedestrian-vehicular accidents after controlling for vehicular and pedestrian flows.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".