  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.
