CASTANET: Causality-Aware Spatio-Temporal Adversarial Network Using Traffic Incident Effects
This work proposes CASTANET, which integrates spatio-temporal graph neural networks and causal treatment effect estimation to utilize incident records while mitigating selection bias, and shows that CASTANET reduces RMSE by 4.0% overall compared to the best baseline and by 10.1% on incident-conditioned evaluation.
Toshiya Kitahara, Ryu Shirakami, Koh Takeuchi et al.
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