Traffic flow forecasting is essential for traffic planning, control and management. The main challenge of traffic forecasting tasks is accurately capturing traffic networks' spatial and temporal correlation. Although there are many traffic forecasting methods, most of them still have limitations in capturing spatial and temporal correlations. To improve traffic forecasting accuracy, we propose a new Spatial-temporal forecasting model, namely the Residual Graph Convolutional Recurrent Network (RGCRN). The model uses our proposed Residual Graph Convolutional Network (ResGCN) to capture the fine-grained spatial correlation of the traffic road network and then uses a Bi-directional Gated Recurrent Unit (BiGRU) to model time series with spatial information and obtains the temporal correlation by analysing the change in information transfer between the forward and reverse neurons of the time series data. Our comparative experimental results on two real datasets show that RGCRN improves on average by 20.66% compared to the best baseline model. You can get our source code and data through https://github.com/zhangshqii/RGCRN.
翻译:交通流量预测对交通规划、控制和管理至关重要。交通流量预测任务的主要挑战在于准确捕捉交通网络的空间和时间相关性。虽然交通预测方法很多,但大多数在捕捉空间和时间相关性方面仍然有局限性。为了提高交通预测的准确性,我们提出了一个新的空间时空预测模型,即残余图集经常网(RGCRN) 。模型利用我们拟议的残余图集连流网络(ResGCN) 来捕捉交通道路网络的细微空间相关性,然后利用双向Gened经常单元(BIGRU)用空间信息模拟时间序列,通过分析时间序列数据前向和反向神经之间的信息传输变化来获取时间相关性。我们在两个真实数据集上的比较实验结果表明,RGCRN与最佳基线模型相比,平均改善20.66%。您可以通过 http://github.com/zhangshqii/RGCRN获得我们的源代码和数据。