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/m2 during peak hours, while the level of service for all types of passengers is deemed to be very crowded at 8 pax/m2, at which point additional passengers are not recommended.
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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".