Characterizing pedestrian traffic by hour-of-day periodicities in commercial zones
Bibliographic record
Abstract
The current state of pedestrian traffic monitoring is characterized by short-duration counts over inconsistent time intervals, making it difficult to compare data temporally at a location or spatially between different locations. Practitioners require understanding of hourly pedestrian traffic periodicities in order to maximize the utility of their short-duration counts. This research deployed six automated pedestrian counters at 12 study sites representing six roadway segments in Winnipeg’s commercial zones. Pedestrian traffic data was collected in 2012 over the summer and fall seasons. This research analyzes the influence of temporal and spatial factors on hourly pedestrian traffic periodicities to enable the characterization of hourly pedestrian traffic in commercial zones. Results indicate that short-duration counts be collected from Tuesday to Thursday on days with less than four hourly precipitation events. Additionally, pedestrian traffic varies seasonally and between adjacent sidewalks in commercial zones. Finally, characterization of pedestrian traffic pattern groups requires detailed land-use data.
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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".