A bi-directional agent-based pedestrian microscopic model
Why this work is in the frame
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Bibliographic record
Abstract
This paper describes the development of a pedestrian microsimulation model that was developed based on the agent based modeling approach, which effectively accounts for the pedestrian intelligence and heterogeneity. The model focuses on producing accurate trajectories for pedestrian interactions. Behavior rules that control pedestrian interactions were extracted from a detailed pedestrian behavior study conducted in Vancouver, BC. The calibration of model parameters was performed using a Genetic algorithm, which aimed at minimizing the error between simulated trajectories and real trajectories obtained by means of computer vision. The validation of the results was conducted using two different data sets. The average errors between simulated and actual trajectories for the two data sets were 35 cm and 27 cm, respectively, while the average speed errors were 13.3% and 5.1%. Results also showed that the model was capable of predicting the correct collision avoidance strategy in 95% of the validation cases investigated.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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