Network traffic forecasting for non-ticketed events

Abstract

An example method includes estimating a number of people expected to attend a non-ticketed event, based on electronic data including social media postings, estimating a per-person amount of network traffic expected to be generated on a communications network during the non-ticketed event, based at least in part on historical per-person network traffic statistics for a historical event of a similar type to the non-ticketed event, calculating an amount of total network traffic expected to be generated during the non-ticketed event, based at least on the number of people expected to attend the non-ticketed event and the per-person amount of network traffic, and implementing a modification to an infrastructure of the communications network in a geographic location of the non-ticketed event, based at least in part on the amount of total network traffic expected to be generated during the non-ticketed event.

What is claimed is: 
     
         1 . A method comprising:
 estimating a number of people expected to attend a non-ticketed event, based on electronic data including social media postings;   estimating a per-person amount of network traffic expected to be generated on a communications network during the non-ticketed event, based at least in part on historical per-person network traffic statistics for a historical event of a similar type to the non-ticketed event;   calculating an amount of total network traffic expected to be generated during the non-ticketed event, based at least on the number of people expected to attend the non-ticketed event and the per-person amount of network traffic; and   implementing a modification to an infrastructure of the communications network in a geographic location of the non-ticketed event, based at least in part on the amount of total network traffic expected to be generated during the non-ticketed event.   
     
     
         2 . The method of  claim 1 , wherein the non-ticketed event is a planned event that is set to occur at a future time. 
     
     
         3 . The method of  claim 1 , wherein the non-ticketed event is a planned event that is currently ongoing. 
     
     
         4 . The method of  claim 1 , wherein the non-ticketed event is an unplanned event that is currently ongoing. 
     
     
         5 . The method of  claim 1 , wherein the estimating comprises:
 generating a popularity score for the non-ticketed event, based at least in part on the social media postings; and   calculating the number of people expected to attend the non-ticketed event as the popularity score multiplied by a maximum capacity of the non-ticketed event.   
     
     
         6 . The method of  claim 5 , wherein the popularity score is calculated as (C*comments+L*likes)*100/views, wherein C and L are constants, comments is a number of user comments posted on a social media page associated with a performer associated with the non-ticketed event, likes is a number of user endorsements posted on the social media page associated with the performer, and views is a number of views accumulated by the social media page associated with the performer. 
     
     
         7 . The method of  claim 1 , wherein the number of people expected to attend the non-ticketed event is updated based on real-time estimates of road traffic within a defined geographic area surrounding the non-ticketed event. 
     
     
         8 . The method of  claim 1 , wherein the estimating the per-person amount of network traffic comprises performing a time series estimate using the historical per-person network traffic statistics for the historical event. 
     
     
         9 . The method of  claim 1 , wherein the per-person amount of network traffic is calculated as a function of time as (T(t)+S(t)+R(t)), where S(t) is a seasonality component, T(t) is a trend component as a function of time, and R(t) represents a measure of error, wherein the trend component assumes an exponential growth in the per-person amount of network traffic, and wherein the seasonality component is modeled to consider traffic variation over weekdays. 
     
     
         10 . The method of  claim 1 , wherein the estimating the per-person amount of network traffic comprises using a deep recurrent neural network to model seasonality, burst, and trend components of the historical per-person network traffic statistics for the historical event. 
     
     
         11 . The method of  claim 1 , wherein the modification includes deploying temporary infrastructure and equipment to accommodate the amount of total network traffic expected to be generated during the non-ticketed event. 
     
     
         12 . The method of  claim 11 , wherein the temporary infrastructure and equipment includes a mobile evolved nodeB. 
     
     
         13 . The method of  claim 11 , wherein the temporary infrastructure and equipment includes a distributed antenna system. 
     
     
         14 . The method of  claim 11 , wherein the temporary infrastructure and equipment includes a portable base station. 
     
     
         15 . The method of  claim 1 , wherein the modification includes instantiating a virtual network function. 
     
     
         16 . The method of  claim 1 , wherein the historical event is identified by performing a linear discriminant analysis on the electronic data in order to classify the non-ticketed event. 
     
     
         17 . A system comprising:
 a processor; and   a computer-readable medium storing instructions which, when executed by the processor, cause the processor to perform operations, the operations comprising:
 estimating a number of people expected to attend a non-ticketed event, based on electronic data including social media postings; 
 estimating a per-person amount of network traffic expected to be generated on a communications network during the non-ticketed event, based at least in part on historical per-person network traffic statistics for a historical event of a similar type to the non-ticketed event; 
 calculating an amount of total network traffic expected to be generated during the non-ticketed event, based at least on the number of people expected to attend the non-ticketed event and the per-person amount of network traffic; and 
 implementing a modification to an infrastructure of the communications network in a geographic location of the non-ticketed event, based at least in part on the amount of total network traffic expected to be generated during the non-ticketed event. 
   
     
     
         18 . A computer-readable medium storing instructions which, when executed by a processor, cause the processor to perform operations, the operations comprising:
 estimating a number of people expected to attend a non-ticketed event, based on electronic data including social media postings;   estimating a per-person amount of network traffic expected to be generated on a communications network during the non-ticketed event, based at least in part on historical per-person network traffic statistics for a historical event of a similar type to the non-ticketed event;   calculating an amount of total network traffic expected to be generated during the non-ticketed event, based at least on the number of people expected to attend the non-ticketed event and the per-person amount of network traffic; and   implementing a modification to an infrastructure of the communications network in a geographic location of the non-ticketed event, based at least in part on the amount of total network traffic expected to be generated during the non-ticketed event.   
     
     
         19 . The computer-readable medium of  claim 18 , wherein the estimating comprises:
 generating a popularity score for the non-ticketed event, based at least in part on the social media postings; and   calculating the number of people expected to attend the non-ticketed event as the popularity score multiplied by a maximum capacity of the non-ticketed event.   
     
     
         20 . The computer-readable medium of  claim 19 , wherein the popularity score is calculated as (C*comments+L*likes)*100/views, wherein C and L are constants, comments is a number of user comments posted on a social media page associated with a performer associated with the non-ticketed event, likes is a number of user endorsements posted on the social media page associated with the performer, and views is a number of views accumulated by the social media page associated with the performer.