  The IARAI competition Traffic4cast 2021 aims to predict short-term city-wide
high-resolution traffic states given the static and dynamic traffic information
obtained previously. The aim is to build a machine learning model for
predicting the normalized average traffic speed and flow of the subregions of
multiple large-scale cities using historical data points. The model is supposed
to be generic, in a way that it can be applied to new cities. By considering
spatiotemporal feature learning and modeling efficiency, we explore 3DResNet
and Sparse-UNet approaches for the tasks in this competition. The 3DResNet
based models use 3D convolution to learn the spatiotemporal features and apply
sequential convolutional layers to enhance the temporal relationship of the
outputs. The Sparse-UNet model uses sparse convolutions as the backbone for
spatiotemporal feature learning. Since the latter algorithm mainly focuses on
non-zero data points of the inputs, it dramatically reduces the computation
time, while maintaining a competitive accuracy. Our results show that both of
the proposed models achieve much better performance than the baseline
algorithms. The codes and pretrained models are available at
https://github.com/resuly/Traffic4Cast-2021.
