CASTANET: Causality-Aware Spatio-Temporal Adversarial Network Using Traffic Incident Effects
Artificial Intelligence
Summary
The authors study how to predict sudden traffic jams caused by accidents or road problems, which are hard to forecast because these events happen rarely and affect traffic differently depending on the situation. They created a method called CASTANET that uses advanced neural networks combined with techniques to handle biased data about when and where incidents occur. Testing their method with real traffic and accident data from Tokyo, the authors found that CASTANET improved prediction accuracy, especially during severe traffic congestion. This shows their approach better captures the impact of unexpected incidents on traffic.
Authors
Toshiya Kitahara, Ryu Shirakami, Koh Takeuchi, Hisashi Kashima
Abstract
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 occurrence is strongly biased in space and time. To address these challenges, we propose CASTANET, which integrates spatio-temporal graph neural networks and causal treatment effect estimation to utilize incident records while mitigating selection bias. Experiments on real-world traffic data and accident records from Tokyo, which we treat as incidents, show that CASTANET reduces RMSE by 4.0% overall compared to the best baseline and by 10.1% on incident-conditioned evaluation, with gains reaching 14.55% under severe congestion.