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

2026-08-28

AI Summary

为解决突发事故引起的非周期性交通拥堵预测难题，提出CASTANET模型，结合时空图神经网络与因果效应估计方法，有效利用事故记录并减少选择偏差。

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.

Problem

• non-periodic traffic congestion
• sudden incidents
• spatio-temporal

Innovation

• spatio-temporal graph neural networks
• causal treatment effect estimation
• incident-driven congestion