Choosing between stairs and escalators in China: The impact of location, height and pedestrian volume
Bibliographic record
Abstract
OBJECTIVE: This research examines whether Beijing residents are more or less likely than Montréal residents to avoid stair climbing, by replicating a study in Montréal, Canada that measured the impacts of distance between stairs and escalator, height between floors and pedestrian volume on stair climbing rate. METHOD: 15 stairways, 14 up-escalators and 13 down-escalators were selected in 13 publicly accessible settings in Beijing. Distance between the bottom or top of nearest stair and escalator combinations varied from 2.1 m to 114.1 m with height between floors varying from 3.3 m to 21.7 m. Simultaneous counts were conducted on stair and escalator pairs, for a total of 37,081 counted individuals. RESULTS: In the ascent model, pedestrian volume accounted for 16.3% of variance in stair climbing, 16.4% when height was added and 45.1% when distance was added. In the descent model, 40.9% of variance was explained by pedestrian volume, 41.5% when height was added and 45.5% when distance was added. CONCLUSION: Separating stairs and escalator is effective in increasing stair climbing in Beijing, accounting for 29% of the variance in stair climbing, compared with 43% in Montreal. As in the Montreal case, distance has less effect on stair use rate when descending. Overall, 25.4% of Beijingers opted for stairs when ascending compared with 20.3% of Montrealers, and for descending 32.8% and 31.1% respectively.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".