基于变分自编码器的交通流预测算法

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中图分类号： TP399 文献标志码： ADOI： 10. 13705 / j. issn. 1671-6841. 2023166

文章编号： 1671-6841（2025）04-0040-07

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 signal， 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.

Key words： traffic flow prediction; variational autoencoder; spatio-temporal dependence

0 引言

我国城市化进程的加快带来了交通拥堵等问题，准确的交通流预测可以为城市的交通管控与车辆调度提供有价值的建议，减少交通事故发生[ 1]目前交通流预测方法可以大致分为统计学方法、机器学习方法和深度学习方法。（剩余11813字）