Dual Encoding U-Net for Spatio-Temporal Domain Shift Frame Prediction
This addresses the challenge of spatio-temporal domain shift in traffic prediction for urban mobility planning, but appears incremental as it builds on existing U-Net and LSTM methods.
The paper tackles the problem of predicting traffic behavior in a post-COVID environment using pre-COVID mobility data by introducing a lightweight Dual-Encoding U-Net with novel skip-connections between Convolutional LSTM layers, achieving unspecified results as no concrete numbers are provided in the abstract.
The landscape of city-wide mobility behaviour has altered significantly over the past 18 months. The ability to make accurate and reliable predictions on such behaviour has likewise changed drastically with COVID-19 measures impacting how populations across the world interact with the different facets of mobility. This raises the question: "How does one use an abundance of pre-covid mobility data to make predictions on future behaviour in a present/post-covid environment?" This paper seeks to address this question by introducing an approach for traffic frame prediction using a lightweight Dual-Encoding U-Net built using only 12 Convolutional layers that incorporates a novel approach to skip-connections between Convolutional LSTM layers. This approach combined with an intuitive handling of training data can model both a temporal and spatio-temporal domain shift (gitlab.com/alchera/alchera-traffic4cast-2021).