The Library
Estimating mobile traffic demand using Twitter
Tools
Tools
Tools
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 ISSN 2162-2337.
|
PDF
WRAP_07464313.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution. Download (6Mb) | Preview |
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 | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TK Electrical engineering. Electronics Nuclear engineering |
||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > 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: |
|
||||||||
| 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 | ||||||||
| Date of first compliant deposit: | 4 May 2016 | ||||||||
| Date of first compliant Open Access: | 4 May 2016 | ||||||||
| 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) |
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
Downloads
Downloads per month over past year
