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Yang, Bowei, Guo, Weisi, Chen, Bozhong, Yang, Guangpu and Zhang, Jie (2016) Estimating mobile traffic demand using Twitter. IEEE Wireless Communications Letters, 5 (4). pp. 380-383. doi:10.1109/LWC.2016.2561924
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Official URL: http://dx.doi.org/10.1109/LWC.2016.2561924
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 (R2=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.
| Item Type: | Journal Article | ||||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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| Divisions: | Faculty of Science > Engineering | ||||||||
| Library of Congress Subject Headings (LCSH): | Online social networks, Traffic monitoring | ||||||||
| Journal or Publication Title: | IEEE Wireless Communications Letters | ||||||||
| Publisher: | IEEE | ||||||||
| ISSN: | 2162-2337 | ||||||||
| Official Date: | August 2016 | ||||||||
| Dates: |
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| Date of first compliant deposit: | 4 May 2016 | ||||||||
| Volume: | 5 | ||||||||
| Number: | 4 | ||||||||
| Number of Pages: | 4 | ||||||||
| Page Range: | pp. 380-383 | ||||||||
| DOI: | 10.1109/LWC.2016.2561924 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||
| Funder: | Horizon 2020 (European Commission) (H2020), Guo jia zi ran ke xue ji jin wei yuan hui (China) [National Natural Science Foundation of China] (NSFC), Engineering and Physical Sciences Research Council (EPSRC) | ||||||||
| Grant number: | 61501399 (NSFC), 61272467 (NSFC), EP/L016400/1 (EPSRC) |
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