# Transfer Learning: Video Prediction and Spatiotemporal Urban Traffic Forecasting +
## 1 Introduction  
Transfer learning (or domain adaptation) is a modern concept defined as application of principles and models, learned in one setting, for improving solutions in another setting--for example, application of models that developed for video prediction for forecasting of urban traffic flows [1, 2]. In a machine learning context, transferring is usually implemented via adaptation of a trained model by transforming the input feature space to match domain inputs or by replacing the final layer of a model to produce domain-specific outputs. For example, a link between popular problems of text and image classification can be created by developing translator functions for converting text- and image-specific features into a common feature set [3]. Another example of transfer learning in image processing is a model (e.g., convolutional neural network) that is trained for the classification of animals and further fine-tuned for the classification of other types of objects [4]. In a more general context, transfer leaning is not limited by the application of pre-trained models in another area, but also extends to the application of general model architectures, widely used in one domain, for solving problems in another domain. The practical advantage of transfer learning is widely acknowledged: application of pre-trained models or carefully tested model architectures allows saving resources for model training. Resources for model training include both computational power and training data set collection (which can be limited in some not-data-rich domains). At the same time, transfer learning may sufficiently improve the scientificprocess of developing and testing new methodologies; the merging of models and principles from different domains leads to a faster and more focused research process.  
One of the key issues for successful transfer learning is the similarity of the output results, input space, and background principles between domains. Although there are several statistical approaches to matching domain-specific features and problems [5], none of them guard against negative transfer effects that appear when information from another domain degrades the performance of a learner from another [1]. Additionally, manual matching of domain-specific features and outputs keep the feature set interpretable, which is highly important for decision makers.  
In this paper, we provide successful evidence for merging methodologies of two emerging areas: video prediction and spatiotemporal urban traffic forecasting. Video prediction [6] is a popular problem of computer vision, which is devoted to the generation of future video frames on the base of the previous video stream. Recently, the problem has become extremely popular due to the spreading of autonomous robots and self-driving cars, growing computational power, and methodological advances. Spatiotemporal urban traffic forecasting [7] is another emerging domain, which is focused on the prediction of citywide traffic flows and road network states on the base of historical traffic information on linked roads (spatial dimensions), observed for a specified time period (temporal dimension). The problem of spatiotemporal structure identification and traffic forecasting plays a rising role in transport engineering [8]. We state that, despite different domains, methodologies developed for video prediction and spatiotemporal traffic forecasting and related data structures have a high level of similarity, which allows the transfer of them between domains. Further, we provide a brief overview of the historical development of video prediction and spatiotemporal traffic forecasting methodologies and summarize the links between them. In the experimental part of our study, we consider the transferring of video prediction principles and models to the domain of spatiotemporal traffic forecasting (asymmetric transferring). Several methods of video processing are selected and their performance for urban traffic flow forecasting is demonstrated for a large real-world data set.