Investigation of Using Microscopic Traffic Simulation Tools to Predict Traffic Conflicts Between Right-Turning Vehicles and Through Cyclists at Signalized Intersections
Bibliographic record
Abstract
Researchers have been questioning if traffic microsimulation tools can be used for road safety evaluations. This thesis examines if these tools have the potential to predict conflicts between right-turning vehicles and through cyclists at signalized intersections. Moreover, this thesis evaluates if calibrating these models to describe the driving behaviour at signalized intersections significantly improves the conflicts’ prediction. It was found that VISSIM has the potential to predict traffic conflicts of interest. In particular, a moderate correlation was found between real conflicts and simulated conflicts of the default models (r=0.525). Calibrating the model for travel time improved the correlation between real conflicts and simulated conflicts (r=0.618). However, a one-way ANOVA test indicated that the improvement caused by travel time calibration was not significant. It was also found that VISSIM’s prediction accuracy is expected to decrease as either the cyclists’ volume or the product of cyclists’ volume and right-turning vehicles’ volume increase.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".