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With deep learning (DL) outperforming conventional methods for differenttasks, much effort has been devoted to utilizing DL in various domains.Researchers and developers in the traffic domain have also designed andimproved DL models for forecasting tasks such as estimation of traffic speedand time of arrival. However, there exist many challenges in analyzing DLmodels due to the black-box property of DL models and complexity of trafficdata (i.e., spatio-temporal dependencies). Collaborating with domain experts,we design a visual analytics system, AttnAnalyzer, that enables users toexplore how DL models make predictions by allowing effective spatio-temporaldependency analysis. The system incorporates dynamic time warping (DTW) andGranger causality tests for computational spatio-temporal dependency analysiswhile providing map, table, line chart, and pixel views to assist user toperform dependency and model behavior analysis. For the evaluation, we presentthree case studies showing how AttnAnalyzer can effectively explore modelbehaviors and improve model performance in two different road networks. We alsoprovide domain expert feedback.

A Visual Analytics System for Improving Attention-based Traffic Forecasting Models