Abstract
Cellular traffic prediction has always been a great challenge in the communication field. Efficiently and correctly predicting the future mobile traffic can improve communication resource’s scheduling. However, changes in mobile traffic are affected by nearby areas. Therefore, prediction methods should be more sensitive to spatial features. Recently, deep learning methods have shown excellent ability in extracting spatial-temporal features. In this work, mobile traffic prediction approach based on the graph convolution networks is propose. We evaluate our model with the Telecom Italia dataset and compare with other traditional models. Experiments show that our model can significantly improve the prediction accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Liu, Z., Li, Z., Wu, K., Li, M.: Urban traffic prediction from mobility data using deep learning. IEEE Netw. 32(6), 40–46 (2018)
Wang, X., et al.: Spatio-temporal analysis and prediction of cellular traffic in metropolis. IEEE Trans. Mob. Comput. 18(9), 2190–2202 (2019)
Zhao, N., Ye, Z., Pei, Y., Liang, Y.-C., Niyato, D.: Spatial-temporal attention-convolution network for citywide cellular traffic prediction. IEEE Commun. Lett. 24(11), 2532–2536 (2020)
Aceto, G., Bovenzi, G., Ciuonzo, D., Montieri, A., Persico, V., Pescapé, A.: Characterization and prediction of mobile-app traffic using Markov modeling. IEEE Trans. Netw. Serv. Manage. 18(1), 907–925 (2021)
Li, R., Zhao, Z., Zheng, J., Mei, C., Cai, Y., Zhang, H.: The learning and prediction of application-level traffic data in cellular networks. IEEE Trans. Wireless Commun. 16(6), 3899–3912 (2017)
Xu, F., et al.: Big data driven mobile traffic understanding and forecasting: a time series approach. Serv. Comput. 9(5), 796–805 (2016)
Li, R., Zhao, Z., Zhou, X., Palicot, J., Zhang, H.: The prediction analysis of cellular radio access network traffic: from entropy theory to networking practice. IEEE Commun. Mag. 52(6), 234–240 (2014)
Sun, H., Liu, H.X., Xiao, H., He, R.R., Ran, B.: Short term traffic forecasting using the local linear regression model. In: Proceedings of 82nd Annual Meeting of the Transportation Research Board, Washington, DC, USA, Jan 2003, pp. 1–30 (2003)
Rago, A., Piro, G., Boggia, G., Dini, P.: Multi-task learning at the mobile edge: an effective way to combine traffic classification and prediction. IEEE Trans. Veh. Technol. 69(9), 10362–10374 (2020)
Sapankevych, N.I., Sankar, R.: Time series prediction using support vector machines: a survey. IEEE Comput. Intell. Mag. 4(2), 24–38 (2009)
Qiu, C., Zhang, Y., Feng, Z., Zhang, P., Cui, S.: Spatial-temporal wireless traffic prediction with recurrent neural network. IEEE Wireless Commun. Lett. 7(4), 554–557 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Yu, C., Ye, Z., Zhao, N. (2022). Graph Convolution Network for Urban Mobile Traffic Prediction. In: Barolli, L., Chen, HC., Miwa, H. (eds) Advances in Intelligent Networking and Collaborative Systems. INCoS 2021. Lecture Notes in Networks and Systems, vol 312. Springer, Cham. https://doi.org/10.1007/978-3-030-84910-8_23
Download citation
DOI: https://doi.org/10.1007/978-3-030-84910-8_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-84909-2
Online ISBN: 978-3-030-84910-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)