Evaluation of Safety Countermeasures at Intersections Using Microscopic Simulation
Bibliographic record
Abstract
In many jurisdictions, over 40% of all road crashes take place at or near intersections. The need to reduce these crashes has fostered considerable research on the development and evaluation of costeffective countermeasures. Safety engineers have been trying to make decisions affecting safety based on the knowledge extracted from different types of statistical models and/or observational before-after analysis. It is generally recognized that this type of factual knowledge is not easily obtained either statistically or empirically. The use of microscopic traffic simulation over the last two decades has essentially focused on the analysis of system transportation efficiency such as signalized intersections, arterial networks and freeway corridors. The potential of microscopic simulation in traffic safety and traffic conflict analysis was initially recognized by Darzentas et al (1980) and has gained increasing interest in recent years. This paper introduces a micro-level behavioural model to estimate crash potential at intersections for different traffic scenarios and geometric attributes based on deceleration rate to avoid the crash (DRAC) and the maximum available deceleration rate (MADR). The model has been applied to a simple left turn movement for a four-leg unsignalized intersection. For this situation, increases in driver perception and reaction times and reduction in the pavement surface friction were found to increase crash potential significantly. The paper speculates on how the model can be used to provide insights into crash reduction resulting from signalization.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".