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Teger: Spatiotemporal Covariance for Probabilistic Traffic Forecasting
Seyed Mohamad Moghadas, Esther Rodrigo Bonet, Bruno Cornelis +1
Traffic conditions drift -- demand patterns, incident dynamics, and sensor behavior shift over a deployment's lifetime -- so a joint uncertainty estimate fit once at training time…
FreqFlow: Long-term forecasting using lightweight flow matching
Seyed Mohamad Moghadas, Bruno Cornelis, Adrian Munteanu
Multivariate time-series (MTS) forecasting is fundamental to applications ranging from urban mobility and resource management to climate modeling. While recent generative models ba…
Strada-LLM: Graph LLM for traffic prediction
Seyed Mohamad Moghadas, Bruno Cornelis, Alexandre Alahi +1
Traffic forecasting is pivotal for intelligent transportation systems, where accurate and interpretable predictions can significantly enhance operational efficiency and safety. A k…
GINTRIP: Interpretable Temporal Graph Regression using Information bottleneck and Prototype-based method
Ali Royat, Seyed Mohamad Moghadas, Lesley De Cruz +1
Deep neural networks (DNNs) have demonstrated remarkable performance across various domains, but their inherent complexity makes them challenging to interpret. This is especially t…