Can Time Proximity Measures be Used as Safety Indicators in All Driving Cultures?
Bibliographic record
Abstract
Limitations associated with traditional collision-based safety analysis techniques led to a growing interest in the use of surrogate safety measures such as the traffic conflict technique. This interest was facilitated by advances in automated video-based data collection methods that helped to overcome the reliability issues associated with manual collection of data on traffic conflicts. Various objective conflict indicators that measure various spatial and temporal aspects of user proximity are available to measure the severity of traffic events. These time-proximity conflict measures assume that proximity is a surrogate for conflict severity. However, this assumption may not be valid in many driving environments. The objective of this paper is to investigate whether time-proximity conflict measures can be a good indicator of safety in less-organized traffic environments with highly mixed road users. A case study of motorcycle conflicts in a highly congested shared intersection in Shanghai, China, was used as a case study. Traffic conflicts were analyzed with the use of automated video-based analysis techniques. Several traffic conflict indicators designed to detect evasive actions, such as deceleration, jerk, and yaw rate, were recommended as better able to measure traffic conflicts in such traffic environments. The results showed that indicators that measured evasive actions had higher potential to identify motorcycle conflicts in highly mixed, less-organized traffic environments than did time-proximity measures such as the time to collision.
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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 | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".