A Hybrid Stacked Traffic Volume Prediction Approach for a Sparse Road Network
Bibliographic record
Abstract
How to provide accurate and timely traffic flow information has become a hot topic in recent years since they can help schedule trips better and reduce traffic congestion. In previous studies, some machine learning (ML)-based models were proposed to predict the traffic volume at a single road segment/position, and these models performed not bad. However, when applied in a more complicated road network, they show low efficiency or need to pay higher computing costs. To solve this problem, an innovative ML-based model named selected stacked gated recurrent units model (SSGRU), is proposed for predicting the traffic flow through a sparse road network in this paper. There are mainly two parts in this model, one is used to do spatial pattern mining based on linear regression coefficients, and the other one includes a stacked gated recurrent unit (SGRU) which is essential for multi-road traffic flow prediction. A binarytree is adopted to approximate the sparse road network in the suburban area. To evaluate the proposed model, seven different traffic volume data sets recorded at 15-min interval are chosen from the England Highways data set to test our proposed work. The result shows that our model has greater adaptability and higher accuracy than others when applied to a multi-road input infrastructure.
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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 | 0.001 | 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".