Crowding Perception Thresholds of Passengers in Urban Rail Transit: A Study of Differences in Spatiotemporal Dimensions
Bibliographic record
Abstract
This paper focuses on the differences in crowding perception among different types of passengers in trains, aiming to optimize passenger experience and improve the level of service of urban rail transit. Based on data from a passenger survey on the Beijing subway, this paper introduces the concept of Crowding Perception Threshold (CPT) and analyzes the principle of passenger spatiotemporal crowding. Considering factors such as gender, age, travel purpose, and standing density of passengers, the paper constructs a quantitative model of crowding perception using the ordered logit model and proposes a method for classifying the level of service accordingly. The study results indicate that the CPTs for all types of passengers range from 91.8% to 101.6%, with the females, elderly individuals, and noncommuters showing greater sensitivity to crowding. In the temporal dimension, all types of passengers have higher CPTs during peak hours than during off‐peak hours, influenced by passengers’ crowding expectations. In the spatial dimension, the level of service for most types of passengers is considered crowded at standing densities of 6‐7 pax/m 2 during peak hours, while the level of service for all types of passengers is deemed to be very crowded at 8 pax/m 2 , at which point additional passengers are not recommended.
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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".