Spatio-temporal analysis and prediction of cellular traffic in metropolis

Understanding and predicting cellular traffic at large-scale and fine-granularity is beneficial and valuable to mobile users, wireless carriers and city authorities. Predicting cellular traffic in modern metropolis is particularly challenging because of the tremendous temporal and spatial dynamics i...

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Main Authors: WANG, Xu, ZHOU, Zimu, YANG, Zheng, LIU, Yunhao, PENG, Chunyi
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Language:English
Published: Institutional Knowledge at Singapore Management University 2017
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Online Access:https://ink.library.smu.edu.sg/sis_research/4738
https://ink.library.smu.edu.sg/context/sis_research/article/5741/viewcontent/dspace_cover_page.pdf
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spelling sg-smu-ink.sis_research-57412020-01-16T10:40:32Z Spatio-temporal analysis and prediction of cellular traffic in metropolis WANG, Xu ZHOU, Zimu YANG, Zheng LIU, Yunhao PENG, Chunyi Understanding and predicting cellular traffic at large-scale and fine-granularity is beneficial and valuable to mobile users, wireless carriers and city authorities. Predicting cellular traffic in modern metropolis is particularly challenging because of the tremendous temporal and spatial dynamics introduced by diverse user Internet behaviours and frequent user mobility citywide. In this paper, we characterize and investigate the root causes of such dynamics in cellular traffic through a big cellular usage dataset covering 1.5 million users and 5,929 cell towers in a major city of China. We reveal intensive spatio-temporal dependency even among distant cell towers, which is largely overlooked in previous works. To explicitly characterize and effectively model the spatio-temporal dependency of urban cellular traffic, we propose a novel decomposition of in-cell and inter-cell data traffic, and apply a graph-based deep learning approach to accurate cellular traffic prediction. Experimental results demonstrate that our method consistently outperforms the state-of-the-art time-series based approaches and we also show through an example study how the decomposition of cellular traffic can be used for event inference. 2017-10-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/4738 info:doi/10.1109/ICNP.2017.8117559 https://ink.library.smu.edu.sg/context/sis_research/article/5741/viewcontent/dspace_cover_page.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Digital Communications and Networking Software Engineering
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Digital Communications and Networking
Software Engineering
spellingShingle Digital Communications and Networking
Software Engineering
WANG, Xu
ZHOU, Zimu
YANG, Zheng
LIU, Yunhao
PENG, Chunyi
Spatio-temporal analysis and prediction of cellular traffic in metropolis
description Understanding and predicting cellular traffic at large-scale and fine-granularity is beneficial and valuable to mobile users, wireless carriers and city authorities. Predicting cellular traffic in modern metropolis is particularly challenging because of the tremendous temporal and spatial dynamics introduced by diverse user Internet behaviours and frequent user mobility citywide. In this paper, we characterize and investigate the root causes of such dynamics in cellular traffic through a big cellular usage dataset covering 1.5 million users and 5,929 cell towers in a major city of China. We reveal intensive spatio-temporal dependency even among distant cell towers, which is largely overlooked in previous works. To explicitly characterize and effectively model the spatio-temporal dependency of urban cellular traffic, we propose a novel decomposition of in-cell and inter-cell data traffic, and apply a graph-based deep learning approach to accurate cellular traffic prediction. Experimental results demonstrate that our method consistently outperforms the state-of-the-art time-series based approaches and we also show through an example study how the decomposition of cellular traffic can be used for event inference.
format text
author WANG, Xu
ZHOU, Zimu
YANG, Zheng
LIU, Yunhao
PENG, Chunyi
author_facet WANG, Xu
ZHOU, Zimu
YANG, Zheng
LIU, Yunhao
PENG, Chunyi
author_sort WANG, Xu
title Spatio-temporal analysis and prediction of cellular traffic in metropolis
title_short Spatio-temporal analysis and prediction of cellular traffic in metropolis
title_full Spatio-temporal analysis and prediction of cellular traffic in metropolis
title_fullStr Spatio-temporal analysis and prediction of cellular traffic in metropolis
title_full_unstemmed Spatio-temporal analysis and prediction of cellular traffic in metropolis
title_sort spatio-temporal analysis and prediction of cellular traffic in metropolis
publisher Institutional Knowledge at Singapore Management University
publishDate 2017
url https://ink.library.smu.edu.sg/sis_research/4738
https://ink.library.smu.edu.sg/context/sis_research/article/5741/viewcontent/dspace_cover_page.pdf
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