论文标题
基于Fastgcrnn的城市交通流量预测
Urban Traffic Flow Forecast Based on FastGCRNN
论文作者
论文摘要
交通预测是在城市交通网络中应用智能运输系统的重要先决条件。现有的作品采用了RNN和CNN/GCN，其中GCRN是最先进的工作，以表征交通流的时间和空间相关性。但是，由于较高的计算复杂性，很难将GCRN应用于大规模的道路网络。为了解决这个问题，我们建议将道路网络抽象成几何图形，并建立快速的图形卷积复发性神经网络（FastGCRNN），以建模交通流量的时空依赖性。具体而言，我们使用FASTGCN单元有效地捕获图表中道路和周围道路之间的拓扑关系，并通过重要性采样来降低计算复杂性，结合GRU单元以捕获交通流量的时间依赖性，并将时空级时嵌入到基于Encoder-Decoder-Decoder-Decoder-Decoder-Decoder框架中的时空特征中。大规模流量数据集的实验表明，所提出的方法可以大大降低计算复杂性和记忆消耗，同时保持相对较高的精度。
Traffic forecasting is an important prerequisite for the application of intelligent transportation systems in urban traffic networks. The existing works adopted RNN and CNN/GCN, among which GCRN is the state of art work, to characterize the temporal and spatial correlation of traffic flows. However, it is hard to apply GCRN to the large scale road networks due to high computational complexity. To address this problem, we propose to abstract the road network into a geometric graph and build a Fast Graph Convolution Recurrent Neural Network (FastGCRNN) to model the spatial-temporal dependencies of traffic flow. Specifically, We use FastGCN unit to efficiently capture the topological relationship between the roads and the surrounding roads in the graph with reducing the computational complexity through importance sampling, combine GRU unit to capture the temporal dependency of traffic flow, and embed the spatiotemporal features into Seq2Seq based on the Encoder-Decoder framework. Experiments on large-scale traffic data sets illustrate that the proposed method can greatly reduce computational complexity and memory consumption while maintaining relatively high accuracy.
