A Novel Hybrid Model for Short-Term Traffic Flow Prediction Based on Spatio-Temporal Deep Learning With Considering Associated Factors Selection
Bibliographic record
Abstract
Effective predicting of traffic flow is the key to fully utilizing the carrying capacity of roads and improving the travel experience. In order to overcome the randomness and volatility of traffic flow, a novel hybrid model for short-term traffic flow prediction based on spatio-temporal deep learning with considering associated factors selection is proposed. In this model, traffic flow with high spatial relevance is identified firstly with the use of the Pearson Product-Moment Correlation Coefficient (PPMCC), then associated factors affecting traffic flow are screened with the use of Random Forest (RF), finally, the results of the two steps mentioned above and the historical traffic flow data are used as input, and the Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit (BiGRU) are used to perceive the spatiotemporal characteristics of traffic flow and the hidden relationships between various factors and traffic flow to predict traffic flow. To test the performance of the proposed model, seven baseline models proposed in the existing literature are compared on publicly available datasets using four indexes to evaluate the performance of the models. In addition, we conducted an ablation study. The results showed that the proposed model has a 39% decrease in root mean square error and a 37% decrease in mean absolute error compared to the baseline models with the best performance.
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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 | 0.001 |
| Open science | 0.002 | 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".