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

@article{Jin2022AVA,
  title={A Visual Analytics System for Improving Attention-based Traffic Forecasting Models},
  author={Seungmin Jin and Hyunwoo Lee and Cheonbok Park and Hyeshin Chu and Yunwon Tae and Jaegul Choo and Sungahn Ko},
  journal={IEEE transactions on visualization and computer graphics},
  year={2022},
  volume={PP}
}
With deep learning (DL) outperforming conventional methods for different tasks, much effort has been devoted to utilizing DL in various domains. Researchers and developers in the traffic domain have also designed and improved DL models for forecasting tasks such as estimation of traffic speed and time of arrival. However, there exist many challenges in analyzing DL models due to the black-box property of DL models and complexity of traffic data (i.e., spatio-temporal dependencies… 

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