  Finding sustainable and novel solutions to predict city-wide mobility
behaviour is an ever-growing problem given increased urban complexity and
growing populations. This paper seeks to address this by describing a traffic
frame prediction approach that uses Convolutional LSTMs to create a Temporal
Autoencoder with U-Net style skip-connections that marry together recurrent and
traditional computer vision techniques to capture spatio-temporal dependencies
at different scales without losing topological details of a given city.
Utilisation of Cyclical Learning Rates is also presented, improving training
efficiency by achieving lower loss scores in fewer epochs than standard
approaches.
