A unidirectional agent based pedestrian microscopic model
Bibliographic record
Abstract
The goal of this paper is to establish a pedestrian model that is capable of simulating pedestrian movement at the microscopic level under normal conditions. Such a model would be useful in studying pedestrian-related transportation issues that require predicting accurate trajectories of pedestrians. The model utilizes the agent based modeling approach in which a continuous space was used to represent the pedestrian environment. This paper presents the first step of the model development: details of a unidirectional micro-simulation pedestrian model along with its calibration and validation. The model was validated by comparing simulated trajectories with real trajectories extracted from video footage by means of computer vision techniques. Results show that the average location difference between simulated and actual trajectories was 20 cm in the X direction and 40 cm in the Y direction. Results also show that the model is capable of predicting pedestrian speed with relative error of 12.8%.
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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) | 0.000 | 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 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".