Traffic Flow Prediction Based on Long Short Term Memory Network

被引：0
|
作者：
Li, Yongfu [1 ]
Wu, Xiaolong [1 ]
机构：
[1] Chongqing Univ Posts & Telecommun, Coll Comp Sci & Technol, Chongqing 400065, Peoples R China
来源：
2018 CHINESE AUTOMATION CONGRESS (CAC) | 2018年
基金：
中国国家自然科学基金;
关键词：
long short term memory; support vector machine; radial basis function; traffic flow predict; NEURAL-NETWORK; MODEL;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This study proposes a traffic flow prediction method based on long short term memory (LSTM) network. Firstly, traffic date is preprocessed by time series method. Then a traffic flow prediction algorithm framework based on LSTM arm was proposed to improve the accuracy of traffic forecast and compare algorithm differences between LSTM, support vector machine (SVM) and radial basis function (RBF). In the last part, a reliable experiment was designed. The experimental results verify the superiority performance of LSTM over SVM and RBF in traffic flow prediction.
引用
收藏
页码：1157 / 1162
页数：6
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