论文标题
带有U-NET样式的临时自动编码器，用于框架预测
Temporal Autoencoder with U-Net Style Skip-Connections for Frame Prediction
论文作者
论文摘要
鉴于城市复杂性和人口不断增长，找到可持续和新颖的解决方案来预测全市范围的流动性行为是一个不断增长的问题。本文旨在通过描述一种使用卷积LSTM的交通框架预测方法来解决这一问题，该方法使用U-NET样式的Skip-Connections创建了暂时的自动编码器，该方法将重复的和传统的计算机视觉技术结合在一起，以捕获不同尺度上的时空依赖性，而不丢失给定城市的拓扑细节。还提出了周期性学习率的利用，从而提高了训练效率，从而提高了比标准方法少的时期损失评分。
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.
