A GNN Approach for Turn-Level Traffic Prediction: Dynamic Relation Awareness and Hypergraph Modeling
Bibliographic record
Abstract
It cannot be emphasized too much to predict traffic flow accurately in modern intelligent transportation systems. Though big progress has been made, few works focus on the turn-level traffic flow prediction, which is important to inspect fine-grained urban traffic dynamics closely. In this work, we develop a GNN (Graph Neural Network) approach built upon Dynamic Relation Awareness and Hypergraph modeling toward turn-level traffic flow prediction, namely DrahGNN. First, we construct a dynamic graph sequence where each snapshot denotes a turn-level traffic flow picture on top of a real-world road network. Second, we innovate a relation-aware spatiotemporal diffusion convolution network to capture road segments’ differences and relatedness explicitly. Third, we construct a hypergraph in each time frame to capture high-order and manifold correlations between road segments and design an attentive two-stage message-passing mechanism for aggregating infor- mation from non-directly connected nodes. We conduct empirical studies on real-world data which demonstrate the effectiveness of our proposed framework.
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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 | 0.001 | 0.002 |
| 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".