cST-ML: Continuous Spatial-Temporal Meta-Learning for Traffic Dynamics Prediction
Author
Abstract
Urban traffic status (e.g., traffic speed and volume) is highly dynamic in nature, namely, varying across space and evolving over time. Thus, predicting such traffic dynamics is of great importance to urban development and transportation management. However, it is very challenging to solve this problem due to spatial-temporal dependencies and traffic uncertainties.
Year of Publication
2020
Conference Name
2020 IEEE International Conference on Data Mining (ICDM)
Date Published
11