Large-scale cellular traffic prediction based on graph convolutional networks with transfer learning

Zhou, Xu; Zhang, Yong; Zhao, Li; Wang, Xing; Zhao, Juan; Zhang, Zhao

doi:10.1007/s00521-021-06708-x

Cited by 31 publications

(11 citation statements)

References 20 publications

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“…This particular model is capable of extracting the spatio-temporal correlation and characteristics of intercellular traffic, leading to highly accurate predictions regarding network traffic patterns. Building upon the foundation of GNN, Zhou, X., et al [28] proposed a transfer learning strategy. The incorporation of this strategy yields substantial benefits, such as preserved computing resources and enhanced model fitting speed, ultimately resulting in the reduced operational time of the model.…”

Section: Related Workmentioning

confidence: 99%

DBSTGNN-Att: Dual Branch Spatio-Temporal Graph Neural Network with an Attention Mechanism for Cellular Network Traffic Prediction

Cai,

Tan,

Zhang

et al. 2024

Applied Sciences

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As network technology continues to develop, the popularity of various intelligent terminals has accelerated, leading to a rapid growth in the scale of wireless network traffic. This growth has resulted in significant pressure on resource consumption and network security maintenance. The objective of this paper is to enhance the prediction accuracy of cellular network traffic in order to provide reliable support for the subsequent base station sleep control or the identification of malicious traffic. To achieve this target, a cellular network traffic prediction method based on multi-modal data feature fusion is proposed. Firstly, an attributed K-nearest node (KNN) graph is constructed based on the similarity of data features, and the fused high-dimensional features are incorporated into the graph to provide more information for the model. Subsequently, a dual branch spatio-temporal graph neural network with an attention mechanism (DBSTGNN-Att) is designed for cellular network traffic prediction. Extensive experiments conducted on real-world datasets demonstrate that the proposed method outperforms baseline models, such as temporal graph convolutional networks (T-GCNs) and spatial–temporal self-attention graph convolutional networks (STA-GCNs) with lower mean absolute error (MAE) values of 6.94% and 2.11%, respectively. Additionally, the ablation experimental results show that the MAE of multi-modal feature fusion using the attributed KNN graph is 8.54% lower compared to that of the traditional undirected graphs.

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Section: Related Workmentioning

confidence: 99%

DBSTGNN-Att: Dual Branch Spatio-Temporal Graph Neural Network with an Attention Mechanism for Cellular Network Traffic Prediction

Cai,

Tan,

Zhang

et al. 2024

Applied Sciences

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“…For the combination scheme of CNN, GCN and attention mechanism, Guo et al's attention-based model leverages GCN and CNN to capture spatiotemporal correlations in traffic data [31]. Yao et al [20] and Zhou et al [32] designed attention-based and GCN-based model to predict traffic statement, resulting in well predictive accuracy in traffic statement prediction.…”

Section: Related Workmentioning

confidence: 99%

Spatial-Temporal Feature Extraction and Evaluation Network for Citywide Traffic Condition Prediction

Chu,

Hou,

Jiang

et al. 2024

IEEE Trans. Intell. Veh.

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Traffic condition prediction is crucial for executing traffic control and scheduling tasks within intelligent transportation systems. With diversified data sources, effectively modeling the complex spatial-temporal dependencies in the whole traffic network and predicting nonlinear general traffic condition changes become primary challenges for intelligent transportation systems. In this paper, a double-layer spatial-temporal feature extraction and evaluation (DL-STFEE) model is proposed, aimed at accurately predicting the traffic condition transferring of the whole traffic network. Firstly, a public traffic dataset is processed to extract the spatial and temporal features of vehicles, as well as the clusters of traffic conditions of the entire traffic network. Secondly, a double-layer deep learning traffic condition predictor is proposed. The model incorporates a spatial-temporal feature extraction layer, which leverages the graph convolution network (GCN) and attention mechanism to obtain spatiotemporal features across the whole traffic network. Additionally, a spatial-temporal combination layer employs a high-dimensional self-attention mechanism to integrate features across spatial-temporal combinations, bolstering prediction accuracy. Finally, through rigorous experiments, the contributions of neural network structures on the spatial-temporal feature extraction are comprehensively analyzed and the experiments also validate the effectiveness of traffic condition prediction by the proposed DL-STFEE model.

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“…The prediction results obtained with TSGAN outperformed three standard GNNs and a GRU model in short-term, midterm, and long-term scenarios. In Zhou et al [67], a Spatiotemporal Graph Convolutional Neural Network is presented and leads to better results than many state-of-art forecasting models: historical averaging, vector autoregressive, LSTM, ConvLSTM, GCN-CNN, attention mechanism GCN-CNN, and diffusion CNN-RNN.…”

Section: ) Graph Neural Networkmentioning

confidence: 99%

Forecasting Network Traffic: A Survey and Tutorial With Open-Source Comparative Evaluation

et al. 2023

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This paper presents a review of the literature on network traffic prediction, while also serving as a tutorial to the topic. We examine works based on autoregressive moving average models, like ARMA, ARIMA and SARIMA, as well as works based on Artifical Neural Networks approaches, such as RNN, LSTM, GRU, and CNN. In all cases, we provide a complete and self-contained presentation of the mathematical foundations of each technique, which allows the reader to get a full understanding of the operation of the different proposed methods. Further, we perform numerical experiments based on real data sets, which allows comparing the various approaches directly in terms of fitting quality and computational costs. We make our code publicly available, so that readers can readily access a wide range of forecasting tools, and possibly use them as benchmarks for more advanced solutions.

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Large-scale cellular traffic prediction based on graph convolutional networks with transfer learning

Cited by 31 publications

References 20 publications

DBSTGNN-Att: Dual Branch Spatio-Temporal Graph Neural Network with an Attention Mechanism for Cellular Network Traffic Prediction

DBSTGNN-Att: Dual Branch Spatio-Temporal Graph Neural Network with an Attention Mechanism for Cellular Network Traffic Prediction

Spatial-Temporal Feature Extraction and Evaluation Network for Citywide Traffic Condition Prediction

Forecasting Network Traffic: A Survey and Tutorial With Open-Source Comparative Evaluation

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