Recurrent Autoencoder with Skip Connections and Exogenous Variables for Traffic Forecasting
Proceedings of the NeurIPS 2019 Competition and Demonstration Track, PMLR 123:47-55, 2020.
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 volume, speed and main traffic direction, in a new aggregated way of traffic data presented as videos. Our approach leverages the continuity in a sequence of frames, learning to embed them into a low dimensional space with an encoder and making predictions there using recurrent layers, ensuring good performance through an embedded loss, and then, recovering back spatial dimensions with a decoder using a second loss at a pixel level. 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.