Study on the Simulation and Optimization of Pedestrian Flow in Metro Stations Based on Anylogic
Bibliographic record
Abstract
Metro stations, as the distribution center for metro passengers, queuing and congestion are very serious during peak commuting hours or when there is a sudden influx of passengers. Based on the example of Yuzhi Road Station of Beijing Metro Line 8, this paper analyzes the characteristics of passenger flow distributed in the station, using Anylogic to simulate the behavior of pedestrians and trains in and out of metro stations, the data of security queues and the average speed of pedestrians are calculated to analyze the rationality of the design of metro stations, and to propose the optimization of the number and speed of the security queues, and to check the optimization effect through modeling simulation and data statistics to provide reference for the operation management of metro stations and to avoid pedestrian congestion. Using Anylogic software to simulate the behavior of pedestrians and the entry and exit of trains in the subway station, the data of security queues and the average speed of pedestrians are calculated to analyze the rationality of the design of metro stations, and to propose the optimization of the number and speed of the security queues, and to check the optimization effect through modeling simulation and data statistics to provide reference for the operation management of metro stations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".