Rail Transit Prediction Based on Multi-View Graph Attention Networks
Bibliographic record
Abstract
Traffic prediction is the cornerstone of intelligent transportation system. In recent years, graph neural network has become the mainstream traffic prediction method due to its excellent processing ability of unstructured data. However, the network relationship in the real world is more complex. Multiple nodes and various associations such as different types of stations and lines in rail transit always exist at the same time. In an end-to-end model, the training accuracy will suffer if the same weights are assigned to multiple views. Thus, this paper proposes a framework with multi-view and multi-layer attention, which aims to solve the problem of node prediction involving multiple relationships. Specifically, the proposed model maps multiple relationships into multiple views. A graph convolutional neural network of multiple views with multi-layer attention learns the optimal regression of nodes. Furthermore, the model uses an autoencoder module to alleviate the over-smoothing problem during the training phase. With the historical dataset of Beijing rail transit, the experiment proves that the prediction accuracy of the model is generally better than the baseline traffic prediction algorithms.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".