Skip to content Skip to navigation
University of Warwick
  • Study
  • |
  • Research
  • |
  • Business
  • |
  • Alumni
  • |
  • News
  • |
  • About

University of Warwick
Publications service & WRAP

Highlight your research

  • WRAP
    • Home
    • Search WRAP
    • Browse by Warwick Author
    • Browse WRAP by Year
    • Browse WRAP by Subject
    • Browse WRAP by Department
    • Browse WRAP by Funder
    • Browse Theses by Department
  • Publications Service
    • Home
    • Search Publications Service
    • Browse by Warwick Author
    • Browse Publications service by Year
    • Browse Publications service by Subject
    • Browse Publications service by Department
    • Browse Publications service by Funder
  • Help & Advice
University of Warwick

The Library

  • Login
  • Admin

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

[img]
Preview
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

Request Changes to record.

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
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:
DateEvent
August 2016Published
3 May 2016Available
29 April 2016Accepted
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)

Request changes or add full text files to a record

Repository staff actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics

twitter

Email us: wrap@warwick.ac.uk
Contact Details
About Us