  Traffic flow prediction (TFP) is a fundamental problem of the Intelligent
Transportation System (ITS), as it models the latent spatial-temporal
dependency of traffic flow for potential congestion prediction. Recent
graph-based models with multiple kinds of attention mechanisms have achieved
promising performance. However, existing methods for traffic flow prediction
tend to inherit the bias pattern from the dataset and lack interpretability. To
this end, we propose a Counterfactual Graph Transformer (CGT) model with an
instance-level explainer (e.g., finding the important subgraphs) specifically
designed for TFP. We design a perturbation mask generator over input sensor
features at the time dimension and the graph structure on the graph transformer
module to obtain spatial and temporal counterfactual explanations. By searching
the optimal perturbation masks on the input data feature and graph structures,
we can obtain the concise and dominant data or graph edge links for the
subsequent TFP task. After re-training the utilized graph transformer model
after counterfactual perturbation, we can obtain improved and interpretable
traffic flow prediction. Extensive results on three real-world public datasets
show that CGT can produce reliable explanations and is promising for traffic
flow prediction.
