Traffic Flow Prediction Based on Multi-Spatiotemporal Attention Gated Graph Convolution Network
Bibliographic record
Abstract
Accurate prediction of traffic flow plays an important role in ensuring public traffic safety and solving traffic congestion. Because graph convolutional neural network (GCN) can perform effective feature calculation for unstructured data, doing research based on GCN model has become the main way for traffic flow prediction research. However, most of the existing research methods solving this problem are based on combining the graph convolutional neural network and recurrent neural network for traffic prediction. Such research routines have high computational cost and few attentions on impaction of different time and nodes. In order to improve the accuracy of traffic flow prediction, a gated attention graph convolution model based on multiple spatiotemporal channels was proposed in this paper. This model takes multiple time period data as input and extracts the features of each channel by superimposing multiple gated temporal and spatial attention modules. The final feature vector is obtained by means of weighted linear superposition. Experimental results on two sets of data show that the proposed method has good performance in precision and interpretability.
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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 | 0.000 |
| 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".