Unified Spatio-Temporal Modeling for Traffic Forecasting using Graph\n Neural Network
Bibliographic record
Abstract
Research in deep learning models to forecast traffic intensities has gained\ngreat attention in recent years due to their capability to capture the complex\nspatio-temporal relationships within the traffic data. However, most\nstate-of-the-art approaches have designed spatial-only (e.g. Graph Neural\nNetworks) and temporal-only (e.g. Recurrent Neural Networks) modules to\nseparately extract spatial and temporal features. However, we argue that it is\nless effective to extract the complex spatio-temporal relationship with such\nfactorized modules. Besides, most existing works predict the traffic intensity\nof a particular time interval only based on the traffic data of the previous\none hour of that day. And thereby ignores the repetitive daily/weekly pattern\nthat may exist in the last hour of data. Therefore, we propose a Unified\nSpatio-Temporal Graph Convolution Network (USTGCN) for traffic forecasting that\nperforms both spatial and temporal aggregation through direct information\npropagation across different timestamp nodes with the help of spectral graph\nconvolution on a spatio-temporal graph. Furthermore, it captures historical\ndaily patterns in previous days and current-day patterns in current-day traffic\ndata. Finally, we validate our work's effectiveness through experimental\nanalysis, which shows that our model USTGCN can outperform state-of-the-art\nperformances in three popular benchmark datasets from the Performance\nMeasurement System (PeMS). Moreover, the training time is reduced significantly\nwith our proposed USTGCN model.\n
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".