Short-term traffic flow prediction based on wavelet function and extreme learning machine
Bibliographic record
Abstract
As the traffic flow has the characteristics of non-linear and strong interference, it has different features in different time-frequency domain. The traditional short-term traffic flow forecasting methods have the disadvantages of lower prediction accuracy, harder parameter determination and poorer adaptability. Aiming at above problems, we proposed a short - term traffic flow forecasting algorithm based on the wavelet function and the Extreme Learning Machine (ELM) to optimize the short - term traffic flow forecasting method. Firstly, the activation function of hidden layer neurons in the prediction model of the ELM is optimized according to the denoising principle of the wavelet function. Secondly, the short-term traffic volume prediction model of the ELM is established, and the traffic volume during the evening peak hours of the Canadian Whitemud Drive highway is forecasted. Finally, the results of this paper are compared with ones that predicted by BP neural network model Compared the R2value of 0.7 in this method with the one of 0.5331 in BP neural network, the results show that the proposed method in this paper has better generalization ability and more proper stability than BP neural network has. The prediction results are in good agreement with the desired short - term traffic volume, and the short-term traffic flow can be predicted more efficiently.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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".