Adaptive Graph Spatial Temporal Fourier-Enhanced Transformer Networks for Traffic Prediction
Bibliographic record
Abstract
Traffic prediction is an important component of intelligent transportation systems as it plays a key role in route planning and traffic management. However, traffic flow series present a complex spatial-temporal correlations and nonlinear traffic patterns, predicting traffic accurately is made challenging by this. The current methods are struggling to model the overall trend of traffic flow series and are unable to utilize dynamic information about spatial dependencies. In this paper, we propose an adaptive graph spatial temporal Fourier-enhanced transformer networks (ASTFETN) to tackle the above traffic prediction problems. ASTFETN adopts an encoder-decoder architecture, the encoder and decoder are both composed of multiple spatial-temporal blocks to capture dynamic spatial and nonlinear temporal correlations. Furthermore, there is a transformer attention layer to capture the relationships of historical and future time. Experiments on two datasets, METR-LA and PEMS-BAY, demonstrate that ASTFETN outperforms the state-of-the-art baselines.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".