论文标题
利用UNET进行未来的流量图预测任务交通挑战2020
Utilizing UNet for the future traffic map prediction task Traffic4cast challenge 2020
论文作者
论文摘要
本文介绍了我们基于UNET的“流量4cast Challenge 2020”的实验。与2019年的流量4CAST挑战相似，该任务是在全球三个大型城市的高分辨率图上预测流量流量，方向和速度。我们主要通过基于UNET的深卷积网络进行了实验，其各种组合物的密度连接卷积层，平均池层和最大池层。尝试了三种基本UNET模型类型，并通过平均预测分数或获得中位数来组合预测。我们的方法在今年新建造的挑战数据集中取得了最佳性能。
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.
