Comparison of Time-Proximity and Evasive Action Conflict Measures: Case Studies from Five Cities
Bibliographic record
Abstract
Interest has grown in using traffic conflicts for studying safety from a broader perspective than relying only on collision data. Traffic conflict analysis is typically performed through the calculation of traditional conflict severity measures such as time-to-collision and postencroachment time. These measures rely on road users getting within specific temporal and spatial proximity from each other and therefore assume that proximity is the surrogate for severity. However, this assumption may not be valid in some driving environments where close interactions between road users are common and sudden evasive actions are frequently used to avoid collisions. It is suggested that evasive action–based conflict indicators can assess the analysis in some less-organized traffic environments. This study focused on the severity evaluation of pedestrian conflicts. Pedestrian evasive actions were reflected mainly in variations of spatiotemporal gait parameters (step frequency and step length). The objective was to compare the use of time proximity and evasive action–based conflict indicators in evaluating the severity of pedestrian conflicts in different traffic environments. Video data from intersections in five major cities—Shanghai, China; New Delhi, India; New York City; Doha, Qatar; and Vancouver, British Columbia, Canada—were analyzed with automated computer vision techniques to extract pedestrian-involved conflicts and calculate conflict indicators. Results show that evasive action–based indicators were more effective in identifying and measuring the severity of pedestrian conflicts than time proximity measures in traffic environments such as Shanghai and New Delhi. However, evasive action measures did not show the same potential in Vancouver and Doha, where time proximity measures were more effective.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".