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

Seungmin Jin, Hyunwook Lee, Cheonbok Park

IEEE Transactions on Visualization and Computer Graphics

|September 26, 2022

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This study introduces AttnAnalyzer, a visual analytics system for exploring deep learning (DL) traffic forecasting models. It helps analyze complex spatio-temporal dependencies to improve model performance.

Area of Science:

Traffic forecasting
Visual analytics
Deep learning models

Background:

Deep learning (DL) models excel in various tasks, including traffic forecasting.
Analyzing DL models for traffic prediction is challenging due to their black-box nature and complex spatio-temporal data dependencies.

Purpose of the Study:

To design a visual analytics system, AttnAnalyzer, for exploring DL model predictions in traffic forecasting.
To enable effective spatio-temporal dependency analysis for better understanding of DL model behavior.

Main Methods:

Developed AttnAnalyzer, a visual analytics system integrating dynamic time warping (DTW) and Granger causality tests.
Incorporated multiple views (map, table, line chart, pixel) for dependency and model behavior analysis.

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Main Results:

AttnAnalyzer facilitates exploration of DL model behaviors in traffic forecasting.
Case studies demonstrate improved model performance in two distinct road networks using the system.

Conclusions:

AttnAnalyzer provides a valuable tool for domain experts to analyze and enhance DL traffic forecasting models.
Effective spatio-temporal dependency analysis is crucial for understanding and improving complex DL models in the traffic domain.