计算机与数字工程2024，Vol.52Issue(4)：1251-1256,6.DOI:10.3969/j.issn.1672-9722.2024.04.051
基于双阶段注意力机制循环神经网络的交通流预测
Traffic Flow Prediction Based on Two-Stage Attention Mechanism Recurrent Neural Network
王健 1王峥2
作者信息
- 1. 武汉邮电科学研究院 武汉 430074||南京烽火天地通信科技有限公司 南京 210019
- 2. 南京烽火天地通信科技有限公司 南京 210019
- 折叠
摘要
Abstract
With the development of deep learning,the accuracy of traffic flow forecasting is increasing.This article starts from the perspective of time series forecasting traffic flow,and is based on a two-stage attention mechanism recurrent neural network model(DA-RNN)to solve the current traffic flow.It is difficult to capture the correlation between time data series in the time series forecasting of traffic,which leads to the problem of inaccurate prediction and solves the problem of overfitting in the experiment.This paper conducts experiments based on PEMS04 data and compares the prediction results with the prediction results of LSTM and GRU models.It shows that this time series prediction model has good performance and can provide an effective basis for traffic man-agement and control.关键词
深度学习/循环神经网络/注意力机制/编码器-解码器Key words
deep learning/recurrent neural network/attention mechanism/encoder-decoder分类
交通工程引用本文复制引用
王健,王峥..基于双阶段注意力机制循环神经网络的交通流预测[J].计算机与数字工程,2024,52(4):1251-1256,6.
