基于多通道时空编码器的交通流量预测模型OACSTPCD

Traditional traffic flow prediction models model historical data in terms of time and space,ignoring the internal po-tential temporal periodicity of traffic data and the distance characteristics and similarity spatial characteristics of nodes between traffic networks.Based on this,this paper proposed a multi-channel spatio-temporal encoder model MC-STGNN for traffic flow prediction to improve the accuracy of traffic flow prediction.Firstly,it processed the traffic data into a three channel periodic time series,and encoded the overall sequence data with temporal and adaptive spatial positions to extract dynamic correlations between road network nodes.Secondly,it introduced a multi-heads self-attention mechanism with convolutional structure to capture varying degrees of temporal correlation of periodic data to a greater extent.Finally,it proposed a graph generator to generate a new spatiotemporal map,extracting similarity and distance features between road network nodes,and integrating the spatial information of the original map and the new spatiotemporal map using a gated graph convolutional network.It conducted comprehensive traffic flow prediction experiments for an hour on the highway datasets PEMS03 and PEMS08.The experimental results show that the MC-STGNN model has better performance indicators compared to other baseline models,indicating that the MC-STGNN model has better modeling ability.