# Transfer Learning: Video Prediction and Spatiotemporal Urban Traffic Forecasting +
## 2 Literature Review
### Transferring Methodologies between Video Prediction and Spatiotemporal Urban Traffic Forecasting  
The brief discussion of video prediction and spatiotemporal urban traffic forecasting methodologies, presented above, reveals multiple similarities of data structures, emerging problems, and applied models and algorithms in these areas. We summarized these similarities in Table 1.  
A high level of data structure similarity between video and citywide traffic flows led to the development and application of similar models and algorithms. All of this creates favorable conditions for transfer learning between these applied areas--from the utilization of model specifications and algorithms, tested in one setting, in another area, to the direct application of pre-trained models in both areas. Transfer learning between video prediction and traffic forecasting has significant practical potential. First, a researcher who is working on spatiotemporal traffic forecasting models gains a higher start and higher learning slope by utilizing developed video forecasting models instead of testing a wide set of potential model specifications. Second, there are special cases where both video prediction and traffic flow forecasting are required--for example, a traffic operations center serves city-level traffic forecasting and monitoring video streams from cameras for preventing road accidents, or a vehicle on-board system forecasts traffic flows for routing and predict video streams for autonomous driving. In such cases, it is more commercially attractive to operate and support one model for both tasks instead of having independent models.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **Video Prediction** & **Spatiotemporal Urban Traffic Forecasting** \\ \hline \multirow{3}{*}{Observation Spatial setting Temporal setting Multiple channels for every pixel (e.g., Red, Green, Blue) Huge spatial and temporal dimensions (e.g., 1920 \(\times\) 1080 resolution of 24 frames per second) Data-rich area Data-rich area Spatiotemporal graph Spatiotemporal graph Methodology Type Spatiotemporal ((motion)) Dynamic objects ((motion)) Dynamic objects ((congestion and prediction of motion)) & Road segment Citywide road network \\  & Physics of observed process (e.g., optical flow models) & Analogy with physics of fluids (e.g., kinematic macroscopic traffic flow models) \\  & Patches (stable regions of a video frame) & Clusters (road segments with similar traffic flows) or reservoirs \\  & & Graph-based convolutional ANN; Multivariate time series with a graph-based structure of dependencies \\ \hline \hline \end{tabular}
\end{table}
Table 1: Matching video prediction and urban traffic forecasting methodologies.  
Despite the significant advantages of transfer learning in terms of computational resources and scientific efforts, the number of studies at the intersection of video prediction and spatiotemporal urban traffic forecasting is extremely small. In 2017, [PERSON] et al. [46] and [PERSON] et al. [47] represented urban traffic data in the form of images and applied deep learning models, developed for image processing (with convolutional layers and LSTM components), for their forecasting. [PERSON] et al. [46] represented traffic in a linear spatial setting (an arterial road) in the form of a two-dimensional spatiotemporal contour diagram; [PERSON] et al. [47] utilized geographical coordinates of a citywide road network and traffic state color-coding for representing traffic at a specified time period as an image. [PERSON] et al. [48] also represented traffic data as an image using geographical coordinates and color-coding traffic states, and tested the direct application of pre-trained image processing models for traffic state prediction (the models were fine-tuned after replacing the last fully connected layer of CNN). Finally, [PERSON] [49] tested different approaches for representing traffic data in a video-like form and for the further application of existing ANN architectures for video prediction.