Algorithms, Volume 13, Issue 2 (February 2020) – 20 articles
Cover Story (view full-size image): Transfer learning is a modern concept that focuses on the application of ideas, models, and algorithms developed in one applied area for solving a similar problem in another area. In this paper, we identify links between methodologies in video prediction and spatiotemporal urban traffic forecasting domains. The similarities of the video stream and citywide traffic data structures are discovered, and analogues between historical development and the current states of the methodologies are presented. The idea of transferring video prediction models (spatial filtering techniques and spectral graph convolutional artificial neural networks) to the urban traffic forecasting domain is validated using a large real-world data set. View this paper.
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