MF-AttnBiLSTM: Traffic Flow Prediction via Hybrid Signal Decomposition and Dual-Stream Temporal Attention Learning
Bibliographic record
Abstract
Accurate traffic flow prediction is crucial for intelligent transportation systems supporting emerging applications such as autonomous driving and vehicle-infrastructure cooperation. However, existing methods often struggle to effectively disentangle the inherent trend, seasonal, and noise components within traffic flow data, thereby limiting prediction accuracy. To address this issue, we propose MF-AttnBiLSTM, a novel hybrid framework combining signal processing and temporal attention-based deep learning model through a decompose-then-predict strategy. Our approach first employs moving average to extract the trend component and discrete Fourier transform to isolate dominant seasonal patterns from the residuals. Subsequently, a dual-stream architecture utilizes multi-head self-attention-enhanced bidirectional LSTMs to independently model the temporal dynamics of the decomposed trend and seasonal components. The final prediction aggregates the outputs from both streams. Extensive experiments on PeMS04 and PeMS07 datasets demonstrate that MF-AttnBiLSTM significantly outperforms state-of-the-art baselines and exhibits robustness across varying traffic conditions. Ablation studies further confirm the efficacy of each component, particularly highlighting the significant contribution of the signal decomposition stage to overall performance improvement.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".