Utilizing Specialized Graph Partitioning and Adaptive GNNs: A Comparative Study for Large-Scale Traffic Forecasting
Why this work is in the frame
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Bibliographic record
Abstract
As urban areas grow and transportation networks become increasingly complex, accurate large-scale traffic forecasting is essential for effective Intelligent Transportation Systems (ITS). Traditional statistical and machine learning approaches often fail to capture the spatial-temporal patterns in large-scale transportation data, while conventional deep learning methods struggle with non-Euclidean network structures. Graph Neural Networks (GNNs) address these challenges by modeling road networks as a graph, resulting in improved forecasting. This thesis builds on the state of the art in large-scale traffic prediction. This is done by integrating an Adaptive Graph Convolutional Recurrent Network (AGCRN) with a high-quality, domain-specific graph partitioning tool - the Buffoon-optimized Karlsruhe High Quality Partitioning (KaHIP). Building on the limitations observed in established models like the Diffusion Convolutional Recurrent Neural Network (DCRNN), AGCRN introduces node-adaptive parameters to more effectively learn localized, evolving traffic patterns. To overcome computational challenges associated with large networks, we employ specialized partitioning frameworks. While graph partitioners like METIS has been the standard choice, we demonstrate that the Buffoonoptimized KaHIP - framework produces higher quality partitions, resulting in higher forecasting accuracy. Tests conducted on a California highway network dataset show that the AGCRNKaHIP integration outperforms DCRNN-METIS and baseline statistical methods. The integration delivers lower prediction errors and more stable forecasts. These results highlight the value of using domain-specific partitioning strategies and adaptive GNN architectures for large-scale traffic forecasting.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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