Metro Passenger Flow Prediction with GNN: Station Attribute Influence Analysis
Bibliographic record
Abstract
Metro passenger flow prediction is vital for optimizing scheduling, enhancing passenger experience, ensuring safety, and supporting urban transportation planning and economic development. Differences in station attributes lead to variations in how neighboring stations influence each other, a factor often overlooked in existing research. To address this, we propose a hybrid model, Graph Convolutional Station-Aware Long Short-Term Memory (GCSA-LSTM), which integrates spatial and temporal features with station-specific attributes. The model begins by classifying stations based on their passenger flow characteristics to tailor predictions to station types. It then uses a Graph Convolutional Network (GCN) to construct a metro network structure, dynamically adjusting the influence weights of neighboring stations to capture spatial dependencies. Finally, an LSTM model processes multi-source data to incorporate temporal dynamics, enabling accurate and robust passenger flow predictions. The model's effectiveness is tested on real-world datasets, outperforming traditional machine learning methods. Ablation experiments further confirm the value of its modular design. By leveraging spatial and temporal dynamics and stationspecific attributes, the GCSA-LSTM model enhances prediction accuracy empowering metro operators to make data-driven decisions that improve efficiency.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".