Traffic4cast-Traffic Map Movie Forecasting -- Team MIE-Lab

The recorded traffic was aggregated into 100x100 meters bins and made available as three-channel images. Within these images, the first channel depicts the traffic volume in each cell, the second one the average speed of vehicles, and the third one the majority of vehicles' directions (as one of four cardinal directions). The data spanned a whole year in 5-minute intervals, where certain days were left out from the training data, to be used for prediction and upload to the traffic4cast servers, which then would assess the quality of the prediction. The prediction itself consisted of "three images into the future" (spanning a 15-minute interval), based on the previous hour (12 images). Given the problem formalization, our efforts mostly focused on the application of well-known image processing algorithms, though we also explored various simple baselines, neural networks taking into account spatiotemporal context, as well as more complex network architectures that should be able to take advantage of the fact that the origin of the data stems from probes that move on a known graph. Ultimately, we did not manage to outperform the "simple" application of a widely-used image processing algorithm, which might be a hint that either a lot more research on networks targeted specifically at this problem or a different formulation of the problem altogether is required.