Digital Twin-Empowered Green Mobility Management in Next-Gen Transportation Networks
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Bibliographic record
Abstract
Evolving transportation networks need seamless integration and effective infrastructure utilisation to form the next-generation transportation networks. Also, they should be capable of capturing the traffic flow data at the right time and promptly applying sustainable actions toward emission reduction. However, traditional transportation networks cannot handle right-time updates and act upon the requirements in dynamic conditions. Here, Digital Twin (DT) enables the development of enhanced transportation management via robust modelling and intelligence capabilities. Therefore, we propose a DT-empowered Eco-Regulation (DTER) framework with a novel twinning approach. We define a transport-specific twin sampling rate to catch right-time data in a transportation network. Besides, we perform emission prediction using Multi-Layer Perceptron (MLP), Bidirectional Long Short-Term Memory (Bi-LSTM), and BANE embeddings. We perform Laplacian matrix analysis to cluster the risk zones regarding the emissions. Thereafter, we recommend actions by setting the number of vehicle limits of junctions for high-emission areas according to the outputs of Q-learning. In summary, DTER takes control of the emission with its transport-specific twin sampling rate and automated management of transportation actions by considering the emission predictions. We note DTER achieves 19% more successful right-time data capturing, with 30% reduced query time. Moreover, our hybrid implementation of intelligent algorithms for emission prediction resulted in higher accuracy when compared to baselines. Lastly, the autonomous recommendations of DTER achieved <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 20% decrease in emissions by presenting an effective carbon tracing framework.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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