论文标题
基于图的U-NET模型，用于预测看不见的城市的流量
A Graph-based U-Net Model for Predicting Traffic in unseen Cities
论文作者
论文摘要
准确的交通预测是使诸如重新路由汽车之类的交通管理的关键要素，以减少道路拥堵或通过动态速度限制来调节流量以保持稳定的流量。表示流量数据的一种方法是暂时更改的热图可视化流量的属性，例如速度和音量。在最近的作品中，U-NET模型在热图预测的交通预测上显示了SOTA性能。我们建议将U-NET体系结构与图层相结合，与Vanilla U-NET相比，将空间概括提高到看不见的道路网络。特别是，我们专门将现有的图形操作对地理拓扑敏感，并概括合并和升级操作以适用于图形。
Accurate traffic prediction is a key ingredient to enable traffic management like rerouting cars to reduce road congestion or regulating traffic via dynamic speed limits to maintain a steady flow. A way to represent traffic data is in the form of temporally changing heatmaps visualizing attributes of traffic, such as speed and volume. In recent works, U-Net models have shown SOTA performance on traffic forecasting from heatmaps. We propose to combine the U-Net architecture with graph layers which improves spatial generalization to unseen road networks compared to a Vanilla U-Net. In particular, we specialize existing graph operations to be sensitive to geographical topology and generalize pooling and upsampling operations to be applicable to graphs.
