Validation of an agent-based microscopic pedestrian simulation model in a crowded pedestrian walking environment
Why this work is in the frame
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Bibliographic record
Abstract
This study validates a recently developed agent-based pedestrian micro-simulation model in a crowded walking environment. The model is applied to simulate pedestrian movements at a major street in the downtown Vancouver area. The street was closed for traffic to allow people attending a social event to leave the area safely. The calibration of model parameters is conducted using a Genetic Algorithm that minimizes the error between simulated and actual trajectories, acquired by means of computer vision. Validation results confirm the accuracy of the simulated trajectories, as the average error between the actual and simulated trajectories is found to be 0.28 m, and the average error in walking speed is just 0.06 m/s. Furthermore, results show that the model is capable of reproducing the actual behavior of pedestrians during different interactions with high accuracy (more than 94% for most interactions).
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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) | 0.000 | 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 it