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.
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 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".