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.

翻译：[translated abstract in Chinese] 交通流预测是智能交通系统中的基础问题，其通过建模交通流潜在的时空依赖关系以预测潜在的拥堵情况。近期，融合多种注意力机制的基于图的模型取得了显著性能。然而，现有交通流预测方法往往继承数据集中的偏差模式且缺乏可解释性。为此，我们提出一种专为交通流预测设计的反事实图变换器模型，该模型配备实例级解释器（例如，发现关键子图）。我们在时间维度上设计输入传感器特征的扰动掩码生成器，并在图变换器模块上设计图结构的扰动掩码生成器，从而获得空间与时间的反事实解释。通过搜索输入数据特征和图结构的最优扰动掩码，可为后续的交通流预测任务提取简洁且关键的数据或图边连接。在反事实扰动后对所用图变换器模型进行重新训练，可获得性能提升且可解释的交通流预测结果。在三个真实世界公开数据集上的广泛实验表明，CGT能够生成可靠的解释，在交通流预测领域具有广阔前景。