A Cost Function Approach to the Prediction of Passenger Distribution at the Subway Platform
Bibliographic record
Abstract
This paper proposes a cost function approach to predict the passenger distribution at the platform, which is contributed to provide services to the passenger safety and convenience and also an efficient use of the subway platform. According to the limited observation and field data collection of Beijing Xuanwumen subway station, passenger behaviors and basic attributes at the platform are analyzed. Based on the analysis and investigation, factors including the distance to the waiting area, the passenger density in the visual field, and the length of the waiting area occupied by passengers are put forward as important factors to affect the choice of the waiting area. The determination of the real-time choice defined by these factors is applied to model the passengers’ waiting area choice behaviors. Simulation experiments are run for the model calibration and validation combining with the collected field data. The results show that the passenger distribution which arises from the model is capable of keeping consistent with the actual distribution in the rough. The model is helpful for controlling how heavy carriages are congested and providing suggestions to optimize the layout of platform facilities.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".