Intersection-Level Turning Movement Flow Prediction Using an Adaptive Spatiotemporal Feature Fusion Network
Bibliographic record
Abstract
Accurate and timely prediction of intersection-level turning movement flow (TMF) is critical for intelligent traffic signal control and real-time signal optimization. However, due to the high nonlinearity, complex interactions, and dynamic temporal variability of intersection traffic flow, the prediction task is more challenging than traditional road segment-level traffic flow forecasting. Currently, the majority of intersection-level TMF prediction studies predominantly adopt a single-model methodology for forecasting, often neglecting the potential contributions of exogenous features. To address this gap, this paper proposes a deep learning framework (AFF-GL) that parallelly integrates Residual Graph Convolutional Neural Networks (ResGCN) and Convolutional Neural Networks (ResCNN), with a two-layer Residual Long Short-Term Memory Network (ResLSTM). Firstly, in this framework, an improved ResGCN is designed to extract multi-scale features. Secondly, the ResCNN is constructed to capture time features. Then, an adaptive feature fusion mechanism is proposed to integrate the extracted information from the temporal and spatial relationship inherent in the traffic flow. Additionally, the incorporation of a two-layer ResLSTM further extracts the fusional spatiotemporal features and achieves the final prediction. At last, experimental results conducted on a real-world dataset collected from 15 intersections in Milton, Ontario, Canada, demonstrate that AFF-GL outperforms several existing renowned models in terms of prediction accuracy, stability, and generalization ability.
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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 | 0.000 | 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".