Methodologies for Aggregating Indicators of Traffic Conflict
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Various indicators of objective conflict have been proposed in the literature to measure the severity of traffic events. Objective conflict indicators measure various spatial and temporal aspects of proximity on the premise that proximity is a surrogate for severity. These aspects of severity may be partially overlapping and in some cases independent. Two sets of conflict indicators were used in a study conducted to demonstrate that integration of the severity cues provided by each conflict indicator could be performed to reflect better the true, yet unobservable, severity of traffic events. The first set of conflict indicators required the presence of a collision course common to the interacting road users. The second set measured severity in mere temporal proximity between road users. The study proposes a methodology with which to aggregate the event-level measurements of conflict indicators into a safety index. First, individual conflict indicator measurements are mapped into severity intervals [0, 1]. Second, these severity indices are aggregated to a safety index that includes both individual severities and exposure. The methodology is applied on individual measurements of pedestrian–vehicle conflicts.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it