Multiple Area Feature-aware Spatio-temporal Public Transit Prediction
This paper introduces the ST-MFGCRN model, a spatio-temporal multi-feature-aware graph convolutional recurrent network for improving public transit prediction by integrating multiple regional features and using sentinel attention to handle feature relevance, showing up to 12% and 7% improvement over state-of-the-art models on the Daejeon and TaxiBJ datasets, respectively.
Aug 23, 2024
Spatio-Temporal Road Traffic Prediction using Real-time Regional Knowledge
Proposes a method for mid-term traffic prediction using region-level data, improving road-level prediction accuracy over baselines.
Aug 23, 2021