  Traffic forecasting is an important element of mobility management, an
important key that drives the logistics industry. Over the years, lots of work
have been done in Traffic forecasting using time series as well as
spatiotemporal dynamic forecasting. In this paper, we explore the use of vision
transformer in a UNet setting. We completely remove all convolution-based
building blocks in UNet, while using 3D shifted window transformer in both
encoder and decoder branches. In addition, we experiment with the use of
feature mixing just before patch encoding to control the inter-relationship of
the feature while avoiding contraction of the depth dimension of our
spatiotemporal input. The proposed network is tested on the data provided by
Traffic Map Movie Forecasting Challenge 2021(Traffic4cast2021), held in the
competition track of Neural Information Processing Systems (NeurIPS).
Traffic4cast2021 task is to predict an hour (6 frames) of traffic conditions
(volume and average speed)from one hour of given traffic state (12 frames
averaged in 5 minutes time span). Source code is available online at
https://github.com/bojesomo/Traffic4Cast2021-SwinUNet3D.
