AI User Attendance Forecasting for Venue Flow Management
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Solution Overview
Problem
Existing systems fail to effectively aggregate and utilize data from large venues to manage user movement in real-time, leading to inefficiencies and suboptimal user experiences due to the limited use of gathered data and lack of proactive management.
Innovation Solution
A method utilizing a trained artificial intelligence model to forecast user attendance at points of interest and zones within an area, integrating various data sources to derive meaningful characteristics, and responding proactively to adjust signage, navigation, and environmental conditions to manage user flow.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data from multiple sources is collected and aggregated, then the ability to forecast user attendance and manage movement improves, but the system complexity increases
Solution Approach 1:
The system segments data collection into multiple independent sources (electronic devices, gates, beacons, imaging systems, sales terminals) and processes them through separate modules that aggregate and analyze data independently before integrating results in the AI forecasting model
Solution Approach 2:
A centralized data aggregation and processing layer acts as an intermediary between multiple data sources and the AI forecasting model, cleaning, normalizing, and preparing data from diverse formats before analysis
2Productivity
If real-time data processing and AI forecasting are implemented, then proactive user movement management improves, but computational resources and processing time requirements increase
Solution Approach 1:
The system performs preliminary data aggregation, cleaning, and feature extraction in advance, preparing processed data structures that the AI model can consume directly without heavy real-time computation during critical moments
Solution Approach 2:
The AI model processes data at aggregated parameter levels (e.g., user flow rates, attendance probabilities) rather than processing every individual raw data point, reducing computational complexity while maintaining forecasting accuracy
3Measurement precision
If multiple data sources are integrated and analyzed, then the accuracy of user attendance forecasting improves, but the difficulty of data processing and analysis increases
Solution Approach 1:
The system merges data from multiple sources by converting them into a unified data structure with common fields and formats, then combines them in a centralized database where the AI model processes integrated information as a single coherent dataset
Solution Approach 2:
The system continuously compares AI forecasting predictions with actual user movement data, using the feedback to refine data processing algorithms and improve the accuracy of feature extraction from multiple data sources
Data Source
AI summary
A method of managing user movement in an area is provided. The area comprises a plurality of points of interest and/or zones. The method comprises receiving raw data relating to one or more users in the area and indicative of a geographic location of the one or more users. The method further comprises deriving, using the raw data relating to the one or more users, characterising data relating to one or more derived characteristics of the one or more users. The method further comprises forecasting, using a trained artificial intelligence model and the characterising data of the one or more users, user attendance at one or more of the points of interest and/or zones, and/or a number of users leaving the area, within a predetermined period. The method further comprises responding to the forecasted user attendance.