Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (9): 168-175.DOI: 10.3778/j.issn.1002-8331.2002-0150

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Spatio-Temporal Fully Connected Convolutional Neural Networks for Citywide Cellular Prediction

HUANG Dongyi, YANG Bing, WU Zihao, KUANG Jiayi, YAN Zeming   

  1. School of Software, Yunnan University, Kunming 650500, China
  • Online:2021-05-01 Published:2021-04-29



  1. 云南大学 软件学院,昆明 650500


Accurate prediction of urban cellular traffic flow is very important for the management and public safety of smart cellular network driven by big data in the future,but it is also very challenging. This paper proposes a method based on deep learning—ST-FCCNet to predict cellular traffic in urban areas. A ST-FCCNet unit structure is designed to capture the spatial dependence between any regions in a city. The ST-FCCNet network framework is deployed to model the temporal closeness and periodicity of cellular traffic to capture temporal dependencies. The influence of external factors(time, weather, vacation, etc.) is combined to obtain the final forecast result. In the experimental part, this paper verifies the effectiveness of ST-FCCNet through actual cellular data sets and compared with the existing 4 methods. The results show that the performance of ST-FCCNet is better than all other methods, and the prediction accuracy is improved by 7.50% to 7.76% compared with the start-of-art.

Key words: spatio-temporal date, deep learning, cellular prediction, convolutional neural networks



关键词: 时空数据, 深度学习, 流量预测, 卷积网络