SOURCE-LINKED INTELLIGENCE

CASTANET: Causality-Aware Spatio-Temporal Adversarial Network Using Traffic Incident Effects

arXiv · AI, language, vision and robotics · article · Aug 28, 2026 · UTC

Predicting non-periodic traffic congestion caused by sudden incidents (e.g., accidents and road damage) is crucial for advanced intelligent transportation systems. However, incident-driven congestion is difficult to forecast because incidents are extremely sparse, occur at specific times and locations, and have heterogeneous impacts depending on the traffic context. While recent deep learning approaches have significantly improved periodic traffic forecasting, their performance on non-periodic congestion remains limited, partly because incident records are not explicitly incorporated and their

Read original source ↗ Open in workspace

recordType

paper

region

Global