In this letter, 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 case-study is performed on Greater London covering a 5000 km   $^{2}$    area. The data used includes over 0.6 million geo-tagged Twitter data, over 1 million mobile phone data demand records, and U.K. 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 h on the same day and for the same time in the following 2–3 days.
['Bowei Yang', 'Weisi Guo', 'Bozhong Chen', 'Guangpu Yang', 'Jie Zhang']
Estimating Mobile Traffic Demand Using Twitter