Predicting the Metro Passengers Flow by Long-Short Term Memory

被引：3
|
作者：
Hu, Zhen [1 ]
Zuo, Yi [1 ]
Xue, Zhuyin [1 ]
Ma, Wenting [1 ]
Zhang, Guilin [1 ]
机构：
[1] Nanjing Res Inst Elect & Engn, Sci & Technol Informat Syst Engn Lab, Nanjing 210007, Jiangsu, Peoples R China
来源：
ADVANCES IN COMPUTER SCIENCE AND UBIQUITOUS COMPUTING | 2018年 / 474卷
关键词：
Traffic prediction; LSTM; Neural network;
D O I：
10.1007/978-981-10-7605-3_97
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, LSTM is proposed to predict metro passengers flow to avoid traffic jams for the city governors. The model is validated by manual counted data and the results show that LSTM can report an instructive prediction.
引用
收藏
页码：591 / 595
页数：5
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