Yang, B., Guo, W., Chen, B. et al. (2 more authors) (2016) Estimating Mobile Traffic Demand using Twitter. IEEE Wireless Communications Letters, PP (99). p. 1. ISSN 2162-2337
Abstract
In this paper, the authors show that structured social media data can act as an accurate predictor for wireless data demand patterns at a high spatial-temporal resolution. A casestudy is performed on Greater London covering a 5000km2 area. The data used includes over 0.6 million geo-tagged Twitter data, over 1 million mobile phone data demand records, and UK census data. The analysis shows that social media activity (Tweets/s n) can accurately predict the long-term traffic demand for both the uplink and downlink channels. The relationship between social media activity and traffic demand obeys a power law and the model explains for over 71-79% of the variance in real traffic demand. This is a significant improvement over existing methods of long-term traffic prediction such as census population data (R 2=0.57). The authors also show that social media data can also forward predict short-term traffic demand for up to 2 hours on the same day and for the same time in the following 2-3 days
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This work is licensed under a Creative Commons Attribution 3.0 License. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 04 Jul 2016 15:26 |
Last Modified: | 23 Jun 2023 22:07 |
Published Version: | http://dx.doi.org/10.1109/LWC.2016.2561924 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Refereed: | Yes |
Identification Number: | 10.1109/LWC.2016.2561924 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:100516 |