郑州大学学报（理学版）2025，Vol.57Issue(4)：40-46,87,8.DOI:10.13705/j.issn.1671-6841.2023166
基于变分自编码器的交通流预测算法
The Traffic Flow Prediction Algorithm Based on Variational Autoencoder
摘要
Abstract
In order to solve the problem that the existing traffic flow prediction models could not fully mine the spatio-temporal dependence of complex and dynamic traffic flow data,a traffic flow prediction model based on variational autoencoder(AST-VAE)was proposed.Firstly,the variational inference and residual decomposition mechanism were used to separate the traffic flow signal into hidden diffusion sig-nal,intrinsic signal and random signal.The temporal and spatial correlations in the three signals were then extracted using different learning modules.Finally,the three multi-dimensional features were fused to capture the global spatio-temporal dependence.With two real traffic datasets,the effectiveness and feasibility of the specific modules of the model were analyzed,and the experimental results showed that AST-VAE was always better than the existing models in the traffic flow prediction task,and the error was low,and it had good prediction performance.关键词
交通流预测/变分自编码器/时空依赖Key words
traffic flow prediction/variational autoencoder/spatio-temporal dependence分类
计算机与自动化引用本文复制引用
崔文源,滕飞,贺百胜,胡晓鹏,仇戈..基于变分自编码器的交通流预测算法[J].郑州大学学报（理学版）,2025,57(4):40-46,87,8.基金项目
四川省科技计划资助项目(2022YFG0028) （2022YFG0028）
