TrafPS: A Shapley-based Visual Analytics Approach to Interpret Traffic
This work addresses the need for interpretability in traffic prediction models to aid traffic management and urban planning, representing an incremental improvement over prior methods.
The authors tackled the problem of interpreting black-box deep learning models for traffic flow prediction by developing TrafPS, a visual analytics approach that uses SHAP-based measurements to quantify the impact of flow patterns, with case studies demonstrating its effectiveness in identifying key routes and supporting decision-making.
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.