Short-Term Traffic Flow Prediction Based on Multi-Model by Stacking Ensemble Learning
Bibliographic record
Abstract
Short-term traffic flow prediction based on multi-model combination under Stacking framework is proposed in this paper, associated with frontier theory research of artificial intelligence. Firstly, the mechanism of Stacking ensemble learning is introduced. The XGBoost algorithm constructed by tree model and the deep learning algorithm represented by LSTM are presented. The Stacking based traffic forecasting model embedded various machine learning algorithms is proposed to utilize their diversified strength. In this stacking framework, the XGBoost algorithm, the LSTM algorithm, the support vector regression and k-Nearest Neighbor are chosen as base-learners, and the XGBoost algorithm is chosen as the meta-learner. Finally, the effectiveness of the algorithm is verified by actual traffic flow data which showed that the forecasting results are more accurate when each of base-learner has lower correlation coefficient in stacking. The results indicated the stacking ensemble learning based on multi-model has better prediction performance compared with the traditional single model.
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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 | 0.001 | 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".