基于多通道时空编码器的交通流量预测模型

传统的交通流量预测模型对历史数据进行时空建模，忽略了交通数据的时间周期性内部潜在关系和交通路网间节点的距离特征和相似性空间特征。据此，提出面向交通流量预测的多通道时空编码器模型MC-STGNN，用来提高交通流量预测的准确率。首先将交通数据处理成三通道的周期性时间序列，并对整体的序列数据进行时间位置编码和自适应的空间位置编码，提取路网节点间的动态相关性；其次引入具有卷积结构的多头自我注意力机制，更大程度地捕获周期数据不同程度的时间相关性；最后提出一种图生成器生成新的时空图，提取路网节点间的相似性和距离特征，并利用门控图卷积网络整合原始图和新时空图的空间信息。在高速公路数据集PEMS03和PEMS08上进行一小时的交通流量综合预测实验，结果表明，MC-STGNN模型与其他的基线模型相比，具有更佳的性能指标，说明MC-STGNN模型具有更优的建模能力。

Traffic flow prediction model based on multi-channel spatial-temporal encoder

Traditional traffic flow prediction models model historical data in terms of time and space, ignoring the internal potential 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.