Exploration of Some Key Issues in Developing and Applying Crash–Conflict Models for Signalized Intersections
Bibliographic record
Abstract
The evaluation process for some roads safety strategies, especially innovative ones, may be challenging, as prior information may be non-existent. This challenge could be addressed with the use of surrogate measures, such as traffic conflicts. Statistical models relating crashes to conflicts are fundamental to this application. The aim of the paper’s research was to investigate some key issues related to the development and application of crash–conflict models. Among the issues addressed are the model specification, the very definition of conflicts, model transferability, and application of the models for estimating crash modification factors (CMFs). Issues are addressed with a case study in which traffic conflicts identified from both time to collision and post encroachment time are generated from the microsimulation of four-legged signalized intersections. These conflicts, in addition to the speed of conflicting vehicles, are used to explore the improved statistical relationships between the frequency of crashes and surrogate measures. Transferability of the models to another jurisdiction is also investigated. The results indicate that the inclusion of the speed variable along with conflicts provides stronger relationships than those with conflicts as a standalone variable. In particular, the results confirm the viability of estimating CMFs with the improved models. The transferability investigation results indicate that it is reasonable to apply the models to the other jurisdiction with caution. Importantly, where such an application is valid, a key conclusion is that calibration of the models would not be necessary to estimate CMFs and apply them to estimate the potential safety impact of a contemplated treatment.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".