Optimal Graph Information Fused Graph Attention Network for Traffic Flow Forecasting
Bibliographic record
Abstract
To manage and make decisions about intelligent transportation systems more efficiently, accurate traffic flow forecasting is necessary. Traffic flow forecasting has complex spatial correlation and time dependence. Most current research models are based on a predefined graph structure with a priori knowledge for prediction, which cannot well extract the hidden spatial relationships in traffic data. In this paper, we propose the Optimal Graph Information Fused Graph Attention Network (OGIF‐GAT). Specifically, we learn the actual connections between nodes and the hidden spatial relationships through the multigraph feature fusion structure. Next, we design a new graph attention network (GAT), which improves the problem of ignoring edge features in the graph structure in the traditional GAT model and considers their edge features when estimating the correlation of each neighboring node pair: the effect that the distance factor between neighboring nodes has on the spatial correlation. In addition, we use the temporal hybrid transformer (THT) to learn temporal dependencies. Extensive experiments on four public transportation datasets (PeMS04, PeMS08, PeMS‐BAY, and METR‐LA) demonstrate that our model achieves the optimal level of traffic flow prediction accuracy on all of them and is shown to have strong generalization ability. Compared to STSGCN, the mean absolute error (MAE) decreases by 7.9%, 10.3%, 33.2%, and 19.6%, respectively.
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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 | 0.003 |
| 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 | 0.001 | 0.002 |
| 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".