基于时空多图卷积网络的交通站点流量预测

交通流量预测是智能交通系统中的重要组成部分,但由于交通流量受交通状况、地理位置、时间等多种因素影响,使其具有高度非线性与复杂性,实现精准预测的难度较大。针对交通站点的出入流量预测问题,提出一种基于上下文门控的时空多图卷积网络(CG-STMGCN)模型。根据站点间的相邻关系与流通流量关系构造邻居图与流通流量图表示站点流量之间的邻近相关性与流量依赖性,在两图上分别建立基于上下文门控的时空卷积模块捕获站点流量的时空特征,并使用哈达玛乘积融合两图的输出作为最终预测结果。在真实交通站点数据集上的实验结果表明,CG-STMGCN模型的预测准确性优于同类预测方法,且稳定性更强。

Flow Prediction of Traffic Stations Based on Spatio-Temporal Multi-Graph Convolutional Network

Traffic flow prediction is an important part of intelligent transportation systems.However,due to the influence of traffic conditions,geographical location,time and other factors,traffic flow prediction is highly non-linear and complex,which imposes a great challenge to accurate prediction.This paper proposes a novel Contextual Gated Spatio-Temporal Multi-Graph Convolutional Network(CG-STMGCN)model to predict the inflow and outflow of traffic stations.In this model,the neighborhood graph and the flow-wise graph are constructed based on the adjacent relationships and flow-wise relationships between stations to represent the proximity correlations and flow dependencies between station flows.Then a contextual gated spatio-temporal convolutional module is constructed on two graphs to capture the spatio-temporal features of station flows.Finally,Hadamard product is used to fuse the outputs of the two graphs as the final prediction result.Experimental results on the dataset of real traffic stations show that the proposed CG-STMGCN model outperforms other existing prediction models in terms of prediction performance,and has better stability.