Short-Term Traffic Prediction considering Spatial-Temporal Characteristics of Freeway Flow
Bibliographic record
Abstract
This paper presents a short-term traffic prediction method, which takes the historical data of upstream points and prediction point itself and their spatial-temporal characteristics into consideration. First, the Gaussian mixture model (GMM) based on Kullback–Leibler divergence and Grey relation analysis coefficient calculated by the data in the corresponding period is proposed. It can select upstream points that have a great impact on prediction point to reduce computation and increase accuracy in the next prediction work. Second, the hybrid model constructed by long short-term memory and K-nearest neighbor (LSTM-KNN) algorithm using transformed grey wolf optimization is discussed. Parallel computing is used in this part to reduce complexity. Third, some meaningful experiments are carried out using real data with different upstream points, time steps, and prediction model structures. The results show that GMM can improve the accuracy of the multifactor models, such as the support vector machines, the KNN, and the multi-LSTM. Compared with other conventional models, the TGWO-LSTM-KNN prediction model has better accuracy and stability. Since the proposed method is able to export the prediction dataset of upstream and prediction points simultaneously, it can be applied to collaborative management and also has good potential prospects for application in freeway networks.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".