Short‐Term Passenger Flow Prediction Based on Federated Learning on the Urban Metro System
Bibliographic record
Abstract
Accurate short‐term metro passenger flow prediction is critical for urban transit management, yet existing methods face two key challenges: (1) privacy risks from centralized data collection and (2) limited capability to model spatiotemporal dependencies. To address these issues, this study proposes a federated learning framework integrating convolutional neural networks (CNNs) and bidirectional gated recurrent units (BIGRU). Unlike conventional approaches that require raw data aggregation, our method facilitates collaborative model training across metro stations while keeping data stored locally. The CNN is employed to extract spatial patterns, such as passenger correlations between adjacent stations, while the BIGRU captures bidirectional temporal dynamics, including peak‐hour evolution. This architecture effectively eliminates the need for sensitive data sharing. We validate the framework using real‐world datasets from Shenzhen Metro, and our key innovations include a privacy‐preserving mechanism through federated parameter aggregation, joint spatial‐temporal feature learning without the need for raw data transmission, and enhanced generalization across heterogeneous 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".