Study on Occupancy Behaviors of Passengers in the Subway Cabin: An Observation in Chengdu, China
Bibliographic record
Abstract
Insufficient attention has been paid to how subway cabins are used by passengers and especially the distribution of passengers and occupancy of facilities. In this study, passengers were observed in 133 sections from the beginning to the end of the early peak of Chengdu Metro in the working days. The differences in occupancy behaviors of passengers to different areas, seats, and standing auxiliary facilities in the cabin were analyzed by the nonparametric test. The occupancy curve was fitted by the least square method from the minimum to the maximum load factor, and the prediction and explanatory model for the use of cabin was established. As expected, the distribution of passengers in the cabin is uneven. The highest occupancy rate has been maintained at the cabin end. Female passengers accounted for the largest proportion in the door area, while male passengers accounted for a larger proportion at the end of the cabin. There is no difference in the use of different seat types by passengers. There are more seats in female passengers, and females are more likely to get the remaining seats when the seat load is nearly saturated. For the auxiliary standing facilities, there are always passengers who do not use the facilities and the proportion is increasing. The facilities that can be relied on account for a greater median proportion of the passengers with facilities, but service capacity is limited. In response to these conclusions, measures to improve the design of the cabin are proposed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".