Research map / Architectures and efficiency / RNNs and normalization
Ising-Traffic: Using Ising Machine Learning to Predict Traffic Congestion under Uncertainty
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add robust training with different levels of uncertainty (ablation) | Reconstruct-Ising · Reconstruction, average across datasets and missing rates | accuracy: 85.67% -> 95.08% | Helped |
| Enable the full set of reconstruction optimizations, including dynamic attention and geographical information embedding (ablation) | Reconstruct-Ising · Reconstruction, average across datasets and missing rates | accuracy: 95.08% -> 98.79% | Helped |
| Use Ising-Traffic rather than the strongest listed baseline | Traffic congestion prediction · PEMS8, 50% missing, test | accuracy: 94.7 -> 99.3 | Helped |
| Use Ising-Traffic rather than the strongest listed baseline | Traffic congestion prediction · PEMS4, 50% missing, test | accuracy: 94.2 -> 99.0 | Helped |
| Use Ising-Traffic rather than the strongest listed baseline | Traffic congestion prediction · PEMS-BAY, 50% missing, test | accuracy: 92.5 -> 99.1 | Helped |
| Use Ising-Traffic instead of the traditional prediction methods | Traffic congestion prediction · PEMS4, PEMS8, and PEMS-BAY | latency: 98× | Helped |
Closest papers by meaning
- WardropNet: Traffic Flow Predictions via Equilibrium-Augmented Learning (ICLR 2025 · similarity 0.85)
- Decomposed Spatio-Temporal Mamba for Long-Term Traffic Prediction (AAAI 2025 · similarity 0.82)
- SSL-STMFormer Self-Supervised Learning Spatio-Temporal Entanglement Transformer for Traffic Flow Prediction (AAAI 2025 · similarity 0.82)
- Efficient Traffic Prediction Through Spatio-Temporal Distillation (AAAI 2025 · similarity 0.82)
- TESTAM: A Time-Enhanced Spatio-Temporal Attention Model with Mixture of Experts (ICLR 2024 · similarity 0.81)
- FairTP: A Prolonged Fairness Framework for Traffic Prediction (AAAI 2025 · similarity 0.81)
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