Traffic Flow Prediction Based on Local Mean Decomposition and Big Data Analysis
Bibliographic record
Abstract
In the era of the big data, the accurate prediction of real-time traffic flow is essential to making rational decisions on travel time, cost and route. To forecast traffic flow accurately, this paper firstly analyzes the features of traffic data, and proves that the traffic data collected from an overpass are self-similar. For simplicity, the long-term correlation (LTC) time series of the traffic data were decomposed into short-term correlation (STC) product functions (PFs) through local mean decomposition (LMD). On this basis, a traffic flow prediction model was developed based on the generalized autoregressive conditional heteroskedasticity (GARCH) model. Simulation results show that our model was more accurate in predicting traffic flow than the original GARCH and the autoregressive integrated moving average (ARIMA) model. Therefore, this research provides a suitable tool for the prediction of traffic flow.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".