# Transfer Learning: Video Prediction and Spatiotemporal Urban Traffic Forecasting +
## 5 Discussion  
Table 3 and Figure 5 demonstrate the comparative forecasting performance of transferred and baseline models. We observed that the overall short-term forecasting performance of the transferred models was effective and comparable with the values of modern spatiotemporal traffic forecasting models. The non-spatial ARIMA model set up a good baseline for one-step-ahead forecasting performance (MAE = 9.02 for \(h=1\)), but its performance was degrading fast for longer forecasting horizons due to its inability to utilize spatial information from neighboring road segments. All spatiotemporal model specifications, except GCNN, demonstrated better stability of performance. The performances of SpX-lm and SpX-SVR models, based on spatial kernels, were the worst among the analyzed spatial model specifications (MAE = 11.27 for SpX-lm and 10.54 for SpX-SVR), but were still reasonable (e.g., better than the performance of the conventional VAR model specification, MAE = 12.61) Although these performance values were not optimal, the SpX-lm and SpX-SVR models had a significant advantage in terms of transferring--their specifications were based purely on the spatial information and did not include temporal information from the analyzed road segment. This means that the obtained models could potentially be transferred to traffic forecasting at spatial locations, where historical information is not available. The SpX-ARIMAX, which includes both temporal and spatial information, demonstrated one of the best performance values among all models (MAE = 8.85). Note that spatial information was included in the SpX-models in a very simple way--by two predefined spatial kernels--and this definition was good enough to successfully compete against the more detailed definition of the spatial structure, applied in SpVAR-models. Comparing SpX-lm and SpX-SVR model specifications, we observed a clear advantage of the SVR-based model, which indicates non-linear patterns of spatial relationships. These non-linearities were partially utilized by the simultaneous usage of two spatial neighborhoods of different radii, \(r_{1}\) and \(r_{2}\). The radii were tuned by cross-validation and set to 10 and 30 min of travel time for all model specifications. They gradually increased with \(step=5\) or longer forecasting horizons (e.g., for \(h=3\) the radii were equal to 20 and 40 min, respectively). Spatial and temporal distributions of MAE values, presented in Figure 5, also indicate the advantages of the SpX-model specifications: comparing the right tails of the distributions (extreme forecasting errors), we observed that the SpX-ARIMAX model provided significantly smaller values. For spatial distribution, it means that the SpX-ARIMAX model was appropriate for all detectors in the research area, while the ARIMA model worked worse for some of them. For temporal distribution, the implication was even more important--the SpX-ARIMAX model gave more stable results for all time periods, including free traffic flow at nights, regular traffic conditions during the day, and congested traffic conditions during peak hours. Overall, we concluded that the approach, based on predefined spatial kernels, widely used for image and video processing, works well for spatiotemporal traffic forecasting. The direct application of the spectral GCNN model for traffic forecasting was not so successful. The performance of the GCNN model was appropriate for one-step-ahead forecasts (MAE = 9.77) but very unstable in terms of extreme error values (RMSE values were significantly higher, given comparable MAE values). There were several possible explanations for these results. First, the conventional GCNN architecture was used for our experiments, so any traffic-specific features were not taken into consideration (e.g., road capacity, weather conditions, etc.). Second, the training process of GCNN was also specific to video prediction and differed from the other models.