计算机应用研究2024，Vol.41Issue(1)：83-87,93,6.DOI:10.19734/j.issn.1001-3695.2023.06.0209
基于多通道时空编码器的交通流量预测模型
Traffic flow prediction model based on multi-channel spatial-temporal encoder
摘要
Abstract
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.关键词
交通流量预测/编码器/空间位置编码/注意力机制/图生成器Key words
traffic flow prediction/encoder/spatial position coding/attention mechanism/graph generator分类
信息技术与安全科学引用本文复制引用
张安勤,秦添..基于多通道时空编码器的交通流量预测模型[J].计算机应用研究,2024,41(1):83-87,93,6.基金项目
广东省人文社会科学重点研究基地——汕头大学地方政府发展研究所开放基金资助项目(07422002) （07422002）
