Bibliographic record
Abstract
Recently, there has been growing interest in using microsimulation models to assess the safety of road facilities by analyzing vehicle trajectories and estimating conflict indicators. Using microsimulation models in safety studies can have several advantages, although concerns have been raised about the ability of these models to represent unsafe vehicle interactions and near misses realistically as well as their need for rigorous calibration. The main objective of this study was to investigate the relationship between field-measured and simulated conflicts at an urban signalized intersection in Surrey, British Columbia, Canada. Sixty hours of recorded traffic data were collected in 2 days and used in the conflict analysis. Automated video-based computer vision techniques were used to extract vehicle trajectories and identify conflicts on all four approaches to the intersection. Conflict measures (e.g., time to collision) and location were determined and compared with simulated conflicts from a microscopic simulation model (VISSIM) using the Surrogate Safety Assessment Model (SSAM). A two-step calibration procedure was proposed to enhance correlation between simulated and field-measured conflicts. The first calibration step was matching actual field conditions (desired speed and arrival type) to ensure that VISSIM gives real average delay values. The second step was the use of sensitivity analysis followed by a genetic algorithm procedure to calibrate the VISSIM parameters that had the biggest effect on the simulated conflicts. Finally, conflict heat maps were provided to compare field-measured with simulated conflict locations. The results highlighted the importance of model calibration and identified several limitations of the SSAM.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".