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Open AccessArticle

Transfer Learning: Video Prediction and Spatiotemporal Urban Traffic Forecasting

Transport and Telecommunication Institute, LV-1019, Riga, Latvia
This paper is an extended version of our paper published in the Proceedings of the 6th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (Cracow, Poland, 5–7 June 2019).
Algorithms 2020, 13(2), 39; https://doi.org/10.3390/a13020039
Received: 9 January 2020 / Revised: 6 February 2020 / Accepted: 11 February 2020 / Published: 13 February 2020
(This article belongs to the Special Issue Models and Technologies for Intelligent Transportation Systems)
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 two fields: video prediction and spatiotemporal traffic forecasting. The similarities of the video stream and citywide traffic data structures are discovered and analogues between historical development and modern states of the methodologies are presented and discussed. The idea of transferring video prediction models to the urban traffic forecasting domain is validated using a large real-world traffic data set. The list of transferred techniques includes spatial filtering by predefined kernels in combination with time series models and spectral graph convolutional artificial neural networks. The obtained models’ forecasting performance is compared to the baseline traffic forecasting models: non-spatial time series models and spatially regularized vector autoregression models. We conclude that the application of video prediction models and algorithms for urban traffic forecasting is effective both in terms of observed forecasting accuracy and development, and training efforts. Finally, we discuss problems and obstacles of transferring methodologies and present potential directions for further research.
Keywords: urban traffic flows; spatiotemporal models; data-driven; graph convolutional neural networks; spatial filtering; network-wide forecasts urban traffic flows; spatiotemporal models; data-driven; graph convolutional neural networks; spatial filtering; network-wide forecasts
MDPI and ACS Style

Pavlyuk, D. Transfer Learning: Video Prediction and Spatiotemporal Urban Traffic Forecasting. Algorithms 2020, 13, 39.

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