A Convolutional Long Short-Term Memory Neural Network Based Prediction Model

被引:3
|
作者
Tian, Y. H. [1 ]
Wu, Q. [1 ]
Zhang, Y. [1 ]
机构
[1] Inner Mongolia Univ Technol, Coll Data Sci & Applicat, Hohhot 010080, Peoples R China
关键词
online car-hailing; supply and demand prediction; long short-term memory (LSTM); convolutional neural network (CNN); AdaBound;
D O I
10.15837/ijccc.2020.5.3906
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 recent years, the market demand for online car-hailing service has expanded dramatically. To satisfy the daily travel needs, it is important to predict the supply and demand of online car-hailing in an accurate manner, and make active scheduling based on the predicted gap between supply and demand. This paper puts forward a novel supply and demand prediction model for online carhailing, which combines the merits of convolutional neural network (CNN) and long short-term memory (LSTM). The proposed model was named convolutional LSTM (C-LSTM). Next, the original data on online car-hailing were processed, and the key features that affect the supply and demand prediction were extracted. After that, the C-LSTM was optimized by the AdaBound algorithm during the training process. Finally, the superiority of the C-LSTM in predicting online car-hailing supply and demand was proved through contrastive experiments.
引用
收藏
页码:1 / 12
页数:12
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