Mobility-Adaptive Digital Twin Modeling for Post-Disaster Network Traffic Prediction
Bibliographic record
Abstract
In this paper, we propose a mobility-adaptive digital twin (MADT) framework for network traffic prediction in a post-disaster scenario where some affected terrestrial base stations (BSs) are malfunctional, causing unavailable user traffic data under their coverage areas. The MADT framework consists of three core modules: 1) DT data construction, 2) DT modeling for traffic prediction, and 3) DT model calibration. For the data construction, we generate a distribution of user volumes over a considered region, where a clustered modular mobility model (clustered-Mo<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>) is tailored to characterize the post-disaster user movements and a spatial-temporal-aware-k-nearest-neighbors (STAK)-based data imputation technique is applied to supplement the missing user traffic data under malfunctional BSs. For traffic prediction, a spatial-temporal graph convolutional network (STGCN) is utilized to establish the DT model under a sequence-to-sequence (seq2seq) forecasting architecture. To improve the traffic prediction accuracy, we further develop a residual calibration (ResCAL) model to estimate and calibrate the traffic prediction errors. The MADT framework establishes a complete DT lifecycle, where the DT model performance is continuously monitored and fed back to trigger the model update if the network traffic pattern changes. Experimental results show that the MADT framework outperforms state-of-the-art spatiotemporal prediction schemes in terms of prediction accuracy and adaptation to user mobility, achieving performance comparable to that of the complete-data-trained STGCN (CD-STGCN).
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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".