  This paper describes our UNet based experiments on the Traffic4cast challenge
2020. Similar to the Traffic4cast challenge 2019, the task is to predict
traffic flow volume, direction and speed on a high resolution map of three
large cities worldwide. We mainly experimented with UNet based deep
convolutional networks with various compositions of densely connected
convolution layers, average pooling layers and max pooling layers. Three base
UNet model types are tried and predictions are combined by averaging prediction
scores or taking median value. Our method achieved best performance in this
years newly built challenge dataset.
