Automated Analysis of Walking Behavior: A Case Study from Qatar
Bibliographic record
Abstract
Pedestrian behavior studies are receiving a growing attention as societies become more aware of the importance of active non-motorized modes of travel. Many developing countries are now recognizing the importance of walking to address limitations in health care and road infrastructure resources as well as increase in obesity. Understanding the walking behavior in developing countries is therefore essential to the evaluation of measures associated with walking conditions such as comfortability and efficiency. This study illustrates the automated collection and analysis of pedestrian behavior data including walking speed and the spatio-temporal gait parameters. The data is used to analyze the walking behavior of female pedestrians inside a female-only university campus in Qatar. Furthermore, this microscopic level analysis is used to investigate the pedestrian walking mechanism and the effect of various attributes such as group size, distraction state and garment style on the walking behavior. A comparison with results of a similar study in Vancouver, British Columbia is also conducted.
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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 | 0.018 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".