Research map / Graph learning / Knowledge and temporal graphs
Traffic Flow Prediction with Vehicle Trajectories
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use TrGNN instead of DCRNN | traffic flow prediction · SG-TAXI overall | MAE: 29.01 -> 26.43 | Helped |
| use TrGNN instead of DCRNN | traffic flow prediction · SG-TAXI peak hours | RMSE: 48.88 -> 42.62 | Helped |
| use TrGNN instead of DCRNN | traffic flow prediction · MRT breakdown | MAE: 40.39 -> 34.56 | Helped |
| add trajectory transition, rather than using the static road network (ablation) | TrGNN · SG-TAXI overall | MAE: 27.34 -> 26.43 | Helped |
| add trajectory transition, rather than using the static road network (ablation) | TrGNN · MRT breakdown | MAE: 38.57 -> 34.56 | Helped |
Closest papers by meaning
- Spatio-Temporal Pivotal Graph Neural Networks for Traffic Flow Forecasting (AAAI 2024 · similarity 0.89)
- Efficient Traffic Prediction Through Spatio-Temporal Distillation (AAAI 2025 · similarity 0.88)
- Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning (AAAI 2025 · similarity 0.88)
- Spatiotemporal-aware Trend-Seasonality Decomposition Network for Traffic Flow Forecasting (AAAI 2025 · similarity 0.88)
- Towards Dynamic Spatial-Temporal Graph Learning: A Decoupled Perspective (AAAI 2024 · similarity 0.87)
- HPNet: Dynamic Trajectory Forecasting with Historical Prediction Attention (CVPR 2024 · similarity 0.86)
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