The Effects of Commuter Pedestrian Traffic on the Use of Stairs in an Urban Setting
Bibliographic record
Abstract
PURPOSE: Most public health physical activity guidelines now encourage people to look for opportunities to accumulate physical activity throughout the day. Climbing stairs in lieu of riding escalators is a prime opportunity to make healthier choices that promote active living. The purpose of this investigation was to examine the effects of pedestrian commuter traffic on choices to ride an escalator, walk up an escalator, or walk up adjacent stairs in a busy urban subway station at rush hour. DESIGN: A total of 9766 commuters were observed by two recorders for a 2.5-hour period during the morning rush hour over 8 weeks as to whether the commuters walked up stairs or rode an adjacent escalator in a subway station. The number of observations per 5-minute block was recorded, and an index of commuter traffic was computed. Demographic information and use of escalators/stairs were also recorded. SETTING: An urban subway station with a two-flight staircase adjacent to an escalator. PARTICIPANTS: Adult commuters travelling to work during the morning rush hour. MEASURES: Physical activity choices were examined in relation to commuter traffic. Demographic information, such as age, race, and weight status, were also considered. ANALYSIS: A χ(2) analysis was used to examine differences in proportions across variables of interest. Means were compared by using multivariate analysis of variance, and confidence intervals were computed. RESULTS: During the least-heavy commuter traffic period, only 11.2% of commuters chose to walk up the stairs, whereas significantly more did so during moderate 18.7% and high 20.8% commuter traffic periods (χ(2) = 61.8, p < .001). During low-traffic times, significantly more commuters (21.4%) walked up the escalators compared with moderate-traffic (18.0%) or high-traffic (18.3%) periods. African-American commuters passively rode the escalator more (68.2%) than white commuters (56.7%), and their patterns were less affected by commuter traffic (p < .05). CONCLUSION: Congestion in public places can have a significant effect on opportunities for choosing active versus passive options in moving through public places. Urban planners should consider this when designing facilities in busy locations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".