Visual Approaches to Understanding Pedestrian Safety in Roundabouts
Bibliographic record
Abstract
Although road safety research has traditionally considered driving as the central mode of interest, recent work has turned to non-motorized modes, particularly cycling and walking, to analyze their conditions within traffic flow, and their interaction with vehicles. �Visual Approaches to Understanding Pedestrian Safety in Roundabouts� is a thesis developed by Mario Perdomo where pedestrian safety is targeted as the main object of study. The research includes two separate studies. The first, based on a Stated Preference (SP) research tool, aims to describe the preferences of pedestrians towards design and operational features of roundabouts, an intersection whose construction has become more frequent in recent years in Quebec. This study describes the process of designing, administering and analyzing the SP survey, offering as its main outcome relevant conclusions regarding pedestrian preferences in terms of safety in roundabouts. The use of substitution rates, estimated from the analysis of the SP survey, are suggested as a means to help guide the design of roundabouts with pedestrians in mind. The second study examines pedestrian-vehicle interactions in roundabouts using automatic pedestrian and vehicle tracking with videos. These interactions were analyzed, making it possible to observe actual pedestrian behavior in such intersections. The core of the thesis relies on two scientific papers: one published in Accident Analysis and Prevention journal in 2014; and the other submitted to the Transportation Research Board the same year.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".