Traffic speed prediction is significant for intelligent navigation and congestion alleviation. However, making accurate predictions is challenging due to three factors: 1) traffic diffusion, i.e., the spatial and temporal causality existing between the traffic conditions of multiple neighboring roads, 2) the poor interpretability of traffic data with complicated spatio-temporal correlations, and 3) the latent pattern of traffic speed fluctuations over time, such as morning and evening rush. Jointly considering these factors, in this paper, we present a novel architecture for traffic speed prediction, called Interpretable Causal Spatio-Temporal Diffusion Network (ICST-DNET). Specifically, ICST-DENT consists of three parts, namely the Spatio-Temporal Causality Learning (STCL), Causal Graph Generation (CGG), and Speed Fluctuation Pattern Recognition (SFPR) modules. First, to model the traffic diffusion within road networks, an STCL module is proposed to capture both the temporal causality on each individual road and the spatial causality in each road pair. The CGG module is then developed based on STCL to enhance the interpretability of the traffic diffusion procedure from the temporal and spatial perspectives. Specifically, a time causality matrix is generated to explain the temporal causality between each road's historical and future traffic conditions. For spatial causality, we utilize causal graphs to visualize the diffusion process in road pairs. Finally, to adapt to traffic speed fluctuations in different scenarios, we design a personalized SFPR module to select the historical timesteps with strong influences for learning the pattern of traffic speed fluctuations. Extensive experimental results prove that ICST-DNET can outperform all existing baselines, as evidenced by the higher prediction accuracy, ability to explain causality, and adaptability to different scenarios.

翻译：交通速度预测对于智能导航和缓解拥堵具有重要意义。然而，由于三个因素，实现精准预测面临挑战：1）交通扩散，即多条相邻道路交通状况之间存在的时空因果关系；2）具有复杂时空相关性的交通数据可解释性差；3）交通速度随时间波动的潜在模式（如早晚高峰）。综合考量这些因素，本文提出一种新颖的交通速度预测架构，称为可解释因果时空扩散网络（ICST-DNET）。具体而言，ICST-DNET包含三个模块：时空因果学习（STCL）、因果图生成（CGG）和速度波动模式识别（SFPR）。首先，为建模路网中的交通扩散，提出STCL模块以捕获单条道路上的时间因果关系及每条道路对间的空间因果关系；随后，基于STCL开发CGG模块，从时空角度增强交通扩散过程的可解释性——具体通过生成时间因果矩阵解释每条道路历史与未来交通状态间的时间因果关系，并利用因果图可视化道路对中的扩散过程；最后，为适应不同场景下的交通速度波动，设计个性化SFPR模块选择强影响历史时间步以学习速度波动模式。大量实验结果表明，ICST-DNET在预测精度、因果解释能力及多场景适应性上均超越现有基线方法。