Open AccessA Convolutional Long Short-Term Memory Neural Network Based Prediction Model
Author(s) -
Yanling Tian,
Qi Wu,
Yue Zhang
Publication year - 2020
Publication title -
international journal of computers, communications and control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.422
H-Index - 33
eISSN - 1841-9844
pISSN - 1841-9836
DOI - 10.15837/ijccc.2020.5.3906
Subject(s) - computer science , convolutional neural network , long short term memory , supply and demand , scheduling (production processes) , artificial intelligence , term (time) , process (computing) , key (lock) , demand forecasting , recurrent neural network , machine learning , artificial neural network , operations research , operations management , computer security , physics , quantum mechanics , engineering , economics , microeconomics , operating system
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.