Big-data-generated traffic flow prediction using deep learning and dempster-shafer theory
Why this work is in the frame
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Bibliographic record
Abstract
This work addresses short-term traffic flow prediction by proposing a big-data-based framework. The proposed framework uses data fusion to deal with heterogeneous data generated from various sources. The data are categorized into two types: streams of data and event-based data. In this work, Deep Belief Networks (DBNs) are used to independently predict traffic flow using streams of data, i.e., historical traffic flow and weather data, and event-based data, i.e., tweets. Furthermore, Dempster's conditional rule for updating belief is used to fuse evidence coming from streams of data and event-based data modules to achieve enhanced prediction. The experimental results using real-world data show the merit of the proposed framework compared to the state-of-the-art ones.
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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