MF-TCPV: A Machine Learning and Fuzzy Comprehensive Evaluation-Based Framework for Traffic Congestion Prediction and Visualization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A framework for traffic congestion prediction and visualization based on machine learning and Fuzzy Comprehensive Evaluation named MF-TCPV is proposed in this paper. The framework uses DataX and DataV to implement the integration of multi-source heterogeneous traffic data and the visualization of congestion prediction results. A deep prediction model named LSTM-SPRVM based on deep learning algorithms, machine learning algorithms, and Spark parallelization technology for the prediction of traffic congestion features in the future is proposed. In MF-TCPV, traffic congestion is divided into six levels based on Fuzzy Comprehensive Evaluation and traffic congestion features such as average speed, road occupancy rate, and traffic flow density. MF-TCPV is validated based on the real data of Whitemud Drive in Canada. The experimental results demonstrate that MF-TCPV is capable of predicting the traffic congestion accurately and displaying prediction results visually. LSTM-SPRVM is better than other existing deep learning models in terms of prediction accuracy, and MF-TCPV can intuitively visualize the prediction results of traffic congestion.
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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 it