  Machine learning-based forecasting models are commonly used in Intelligent
Transportation Systems (ITS) to predict traffic patterns and provide city-wide
services. However, most of the existing models are susceptible to adversarial
attacks, which can lead to inaccurate predictions and negative consequences
such as congestion and delays. Therefore, improving the adversarial robustness
of these models is crucial for ITS. In this paper, we propose a novel framework
for incorporating adversarial training into spatiotemporal traffic forecasting
tasks. We demonstrate that traditional adversarial training methods designated
for static domains cannot be directly applied to traffic forecasting tasks, as
they fail to effectively defend against dynamic adversarial attacks. Then, we
propose a reinforcement learning-based method to learn the optimal node
selection strategy for adversarial examples, which simultaneously strengthens
the dynamic attack defense capability and reduces the model overfitting.
Additionally, we introduce a self-knowledge distillation regularization module
to overcome the "forgetting issue" caused by continuously changing adversarial
nodes during training. We evaluate our approach on two real-world traffic
datasets and demonstrate its superiority over other baselines. Our method
effectively enhances the adversarial robustness of spatiotemporal traffic
forecasting models. The source code for our framework is available at
https://github.com/usail-hkust/RDAT.
