Authors:
Brad Johnson, Kevin Xu, Dan Kelaher
Abstract:
This disclosure proposes a system that uses machine learning to accurately predict event attendance from a variety of pre-event social signals (e.g., likes, RSVPs, trending hashtags). It then validates/refines in real-time using aerial drone feeds for crow density, flow, and spatial layout, helping vendors choose where and when to place mobile stores for highest visibility and ROI.
Background:
Vendors lack an accurate way of predicting event turnout, and where the crowd flows from large arenas with many entrances and exits (modern arenas feature up to 8-12). With limited resources, vendors can only selectively target certain locations hoping customer exposure will be high.
Description:
EventIQ integrates social media forecasting with real-time aerial surveillance to optimize mobile store deployment at large-scale venues. Prior to the event, a predictive engine scrapes social media platforms and ticketing APIs to quantify pre-event engagement, analyzing variables such as RSVP volume, post velocity (data collected after an event concluded), geographic origin of attendees, and influencer interactions. A regression model or neural net maps these signals to expected attendance curves and temporal crowd distribution estimates.
During the event, aerial drones equipped with thermal and optical imaging feed live data to a crowd analysis module. This module segments crowd flow by entrance, density clusters, and movement vectors using computer vision techniques like optical flow analysis and density heatmapping.
These two data streams—pre-event prediction and live drone validation—are merged in a geospatial decision engine that determines the optimal positions for mobile retail units. The system can recommend repositioning in real time based on shifting flows (e.g., post-event surges at exit gates). Drone feeds are mapped against venue blueprints to avoid no-fly zones and enforce privacy redactions. The result is a dynamic, data-driven mobile store deployment that maximizes customer exposure and product interaction time.
The following figure demonstrates the idea:
TGCS Reference 00787
