Modeling of Pedestrian Activity at Signalized Intersections: Land Use, Urban Form, Weather, and Spatiotemporal Patterns
Bibliographic record
Abstract
The present study evaluated the effects of land use, urban form patterns, and weather conditions on pedestrian activities. In this research, 8 h of daily manual pedestrian counts during the a.m. peak, noon, and p.m. peak periods were collected from a large sample of signalized intersections throughout Canada. The use of different model settings was attempted as part of a model sensitivity analysis. Results revealed that a 100% increase in population density or the amount of commercial space around intersections increased pedestrian flows by 22.7% to 37.1% and 10.7% to 11.7%, respectively. Moreover, the pedestrian activity at an intersection decreased as the distance from downtown increased (with an elasticity of 44%). Also, very warm weather (with temperatures >30°C) decreased pedestrian activity by up to 22%. This study was the first attempt to develop a spatiotemporal model of pedestrian activity in a large city. These results should be taken with caution, however, because the sample of intersections was not randomly selected. Moreover, the modeling techniques used in this research did not take into account the potential spatial correlation across intersections. This factor may have caused bias in parameter estimates. These issues are part of future work.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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 source (direct Gemma or distilled Codex), 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".