Semiautomated Analysis of Pedestrian Behaviour and Motion for Microsimulation of Transportation Terminals
Bibliographic record
Abstract
Pedestrian Microsimulation is used to design and evaluate the circulation of pedestrians in buildings and pedestrian spaces during the design phase so that changes and optimizations can be made. However, models need input data such as walking speeds which reflect the population modelled. Mass amounts of detailed data collection is therefore needed, but current methodologies are either too slow or use expensive equipment and do not consider finer details. A new methodology called Semiautomated Tracking is created for tracking pedestrians from video footage and generating walking speeds based on manually assigned tags. The methodology is verified against manually calculated movement speeds and applied to a multi-factor analysis of a transportation terminal, revealing that Semiautomated Tracking can quickly generate detailed data for more people and with similar results. Differences were also found between the Canadian data and international guidance, highlighting the importance of the methodology and encouraging future efforts for development.
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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 | 0.002 | 0.001 |
| 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) | 0.002 | 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 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".