基于图自编码器和GRU网络的分层交通流预测模型
Hierarchical Traffic Flow Prediction Model Based on Graph Autoencoder and GRU Network
赵子琪 1杨斌 1张远广1
作者信息
- 1. 南华大学电气工程学院 湖南衡阳 421001
- 折叠
摘要
准确的交通流预测信息不仅可以为交通管理人员提供交通决策的坚实基础,还可以减少交通拥堵情况.在交通流预测任务中,获得有效的交通流的时空特性是保证预测效果的前提.现有的方法大多是用未来时刻的数据进行监督学习,提取的特征具有局限性.针对现有预测模型无法充分挖掘交通流的时空特性的问题,提出了基于改进的图自编码器和门控循环单元的分层交通预测模型.首先使用图注意力自编码器以无监督的方式深度挖掘交通流的空间特性,然后使用门控循环单元进行时间特征提取.分层结构采用分开训练的方式进行时空依赖关系的学习,旨在获取路网天然存在的空间拓扑特征,使其可以兼容不同时间步下的交通流预测任务.大量实验证明,所提出的GAE-GRU模型在不同数据集下的交通预测任务中取得了优异的表现,MAE,RMSE和MAPE指标均优于基线模型.
Abstract
Accurate traffic flow prediction information not only provides traffic administrator with a strong foundation for traffic decisions,but also eases congestion.In traffic flow forecasting tasks,obtaining valid spatiotemporal characteristics of the traffic flow is a prerequisite to ensure the effectiveness of the forecast.Most of the existing methods use data from future moments for supervised learning,and the extracted features have limitations.To address the problem that existing prediction models cannot fully exploit the spatiotemporal characteristics of traffic flows,this paper proposes a hierarchical traffic prediction model based on an improved graph autoencoder and gated recurrent unit.The graph attention autoencoder is first used to deeply explore the spa-tial characteristics of the traffic flow in an unsupervised manner,and then the gated recurrent unit is used to extract temporal fea-tures.The hierarchical structure uses separate training for learning spatio-temporal dependencies,aiming to capture the naturally existing spatial topological features of the road network and make it compatible with traffic flow prediction tasks at different time steps.Extensive experiments demonstrate that the proposed GAE-GRU model achieves excellent performance in traffic prediction tasks on different datasets,with MAE,RMSE and MAPE outperforming the baseline model.
关键词
交通流预测/图自编码器/门控循环单元/分层/时空依赖Key words
Traffic flow forecasting/Graph autoencoder/Gated recurrent neural unit/Hierarchical/Spatiotemporal dependencies引用本文复制引用
出版年
2024
NETL
NSTL
万方数据