基于图卷积时空生成对抗网络的城市交通估计

Urban Traffic Estimation Based on Graph Convolution Spatiotemporal GAN

在城市道路部署前估计路网的交通流量极具挑战性,为了解决这个难题,提出了一种新的条件城市交通生成对抗网络(Curb-GAN)模型,利用条件生成对抗网络(CGAN)生成城市交通流量数据.首先,把路网各节点的距离关系和外部特征信息作为条件处理,来控制生成结果;其次,利用图卷积网络(GCN)捕获路网的空间自相关性,利用自注意力机制(SA)和门控循环单元(GRU)捕获不同时隙交通的时间依赖性;最后,由训练好的生成器生成交通流量数据.在两个真实时空数据集上的大量实验表明,Curb-GAN模型的估计精度优于主要的基线方法,并且可以产生更有意义的估计.

It is very challenging to estimate the traffic flow before urban road deployment.To solve this problem,this study proposes a new conditional urban traffic generating adversarial network(Curb-GAN)model,which utilizes a conditional generating adversarial network(CGAN)to generate urban traffic flow data.Firstly,the distance relationship and external feature information of each node of the road network are treated as conditions to control the generated results.Secondly,the spatial autocorrelation of the road network is captured by the graph convolutional network(GCN),and the time dependence of traffic in different time slots is captured by the self-attention(SA)mechanism and gated cycle unit(GRU).Finally,the trained generator generates traffic flow data.A large number of experiments on two real spatiotemporal datasets show that the estimation accuracy of the Curb-GAN model is superior to the main baseline methods and can produce more meaningful estimates.