Recent achievements in deep learning (DL) have shown its potential for predicting traffic flows. Such predictions are beneficial for understanding the situation and making decisions in traffic control. However, most state-of-the-art DL models are considered "black boxes" with little to no transparency for end users with respect to the underlying mechanisms. Some previous work tried to "open the black boxes" and increase the interpretability of how predictions are generated. However, it still remains challenging to handle complex models on large-scale spatio-temporal data and discover salient spatial and temporal patterns that significantly influence traffic flows. To overcome the challenges, we present TrafPS, a visual analytics approach for interpreting traffic prediction outcomes to support decision-making in traffic management and urban planning. The measurements, region SHAP and trajectory SHAP, are proposed to quantify the impact of flow patterns on urban traffic at different levels. Based on the task requirement from the domain experts, we employ an interactive visual interface for multi-aspect exploration and analysis of significant flow patterns. Two real-world case studies demonstrate the effectiveness of TrafPS in identifying key routes and decision-making support for urban planning.

翻译：深度学习在交通流量预测方面的最新成果显示了其潜力。此类预测有助于理解交通状况并辅助交通控制决策。然而，大多数当前最先进的深度学习模型被视为“黑箱”，终端用户几乎无法了解其底层机制。已有研究尝试“打开黑箱”以增强预测生成过程的可解释性，但在处理大规模时空数据上的复杂模型、发现显著影响交通流量的关键空间与时间模式方面仍存在挑战。为应对这些挑战，我们提出TrafPS——一种用于解释交通预测结果的可视分析方法，以支持交通管理与城市规划中的决策。我们提出了区域SHAP与轨迹SHAP两种度量指标，用于量化不同层面上流模式对城市交通的影响。基于领域专家的任务需求，我们设计了一个交互式可视化界面，支持对显著流量模式进行多角度探索与分析。两个实际案例研究验证了TrafPS在识别关键路径及为城市规划提供决策支持方面的有效性。