A Hybrid Traffic Flow Forecasting and Risk-Averse Decision Strategy for Hydrogen-Based Integrated Traffic and Power Networks
Why this work is in the frame
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Bibliographic record
Abstract
This article develops an operational framework for hydrogen microgrids integrated with traffic and power networks to optimize decision-making strategies. It tackles challenges in traffic flow prediction exacerbated by the rise of electric and hydrogen vehicles, which significantly affect power systems and hydrogen microgrids. We employ a risk-averse information gap decision theory to ensure secure operations under uncertain traffic conditions. Our framework utilizes a hybrid deep-learning forecasting method, combining a 1-D convolutional neural network and bidirectional long short-term memory to accurately predict traffic flow for origin–destination pairs in Edmonton, Canada. Enhanced by a Bayesian algorithm for hyperparameter tuning, this method improves prediction accuracy and operational efficiency. The framework also integrates operational strategies with urban travel plans to optimize charging for electric and hydrogen vehicles, thereby enhancing energy efficiency and supporting thermal demands. Validated in Edmonton's power and traffic networks, our framework enhances optimal charging, routing, and operation conditions, surpassing traditional methods to maintain secure operations during outages and improve the overall system robustness.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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