  The increasing complexity of mobility plus the growing population in cities,
together with the importance of privacy when sharing data from vehicles or any
device, makes traffic forecasting that uses data from infrastructure and
citizens an open and challenging task. In this paper, we introduce a novel
approach to deal with predictions of speed, volume, and main traffic direction,
in a new aggregated way of traffic data presented as videos. The approach
leverages the continuity in a sequence of frames and its dynamics, learning to
predict changing areas in a low dimensional space and then, recovering static
features when reconstructing the original space. Exogenous variables like
weather, time and calendar are also added in the model. Furthermore, we
introduce a novel sampling approach for sequences that ensures diversity when
creating batches, running in parallel to the optimization process.
