3 papers
cs.LG2026
General Semantic Knowledge Infusion for Spatio-Temporal Traffic Forecasting
Mattis thor Straten, Yannick Wolker, Steffen Strohm +3
Although Graph Neural Networks (GNNs) have made significant advances in spatio-temporal traffic forecasting, their performance is limited when they rely solely on sensor proximity…
cs.LG2025
SUSTeR: Sparse Unstructured Spatio Temporal Reconstruction on Traffic Prediction
Yannick Wölker, Christian Beth, Matthias Renz +1
Mining spatio-temporal correlation patterns for traffic prediction is a well-studied field. However, most approaches are based on the assumption of the availability of and accessib…
cs.LG2025
Small Graph Is All You Need: DeepStateGNN for Scalable Traffic Forecasting
Yannick Wölker, Arash Hajisafi, Cyrus Shahabi +1
We propose a novel Graph Neural Network (GNN) model, named DeepStateGNN, for analyzing traffic data, demonstrating its efficacy in two critical tasks: forecasting and reconstructio…