Comparison of Traffic Conflict Indicators for Crash Estimation using Peak Over Threshold Approach
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
Traffic conflict techniques have drawn considerable research interest and a number of conflict indicators have been developed. Previous studies have qualitatively analyzed indicator differences from their definitions and empirically investigated their similarities based on identified traffic conflicts. This study compares conflict indicators from a validity perspective by comparing crashes estimated from conflict indicators with observed crashes. The peak over threshold (POT) approach was employed for crash estimation. Four commonly used indicators are compared: time to collision (TTC), modified time to collision (MTTC), post encroachment time (PET), and deceleration to avoid a crash (DRAC). Based on the conflict and crash data collected from three signalized intersections, POT models are developed for different thresholds in the appropriate ranges, and crash estimation methods were proposed for individual conflict indicators. The identified conflicts and estimated crashes associated with different indicators are then compared. The results show that traffic conflicts identified by the four indicators vary, with MTTC generating the most accurate crash estimates. The crash estimates from TTC and PET are also reasonable but there is a tendency of overestimation for TTC and underestimation for PET. The crash estimates of DRAC are all outside the confidence intervals of observed crashes, which is likely related to the uncertainty of vehicle braking capacity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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