  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).
