Adaptive Radius Crowd Estimation via UE Distribution Analysis
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Solution Overview
Problem
Existing crowd counting methods using mobile communication networks face limitations in accurately determining the number of attendees at public events due to issues with area of interest size, leading to inefficiencies in resource planning and management.
Innovation Solution
A method and system that estimate the number of persons at an Area of Interest by defining an optimal radius based on operational information from user equipment interactions, combining data from event records within the area and previous days to detect public happenings and calculate the number of attendees, using a statistical approach to determine the optimum radius and account for user equipment presence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a fixed radius is used for the Area of Interest, then the system is simple to operate, but the measurement precision of crowd estimation deteriorates due to inaccurate area definition
Solution Approach 1:
The patent applies dynamics by transitioning from a fixed radius to an adaptive radius that changes based on observed user equipment patterns. The system initially uses a default radius but dynamically adjusts it based on the actual spatial distribution of UEs detected during events, thereby optimizing both operational simplicity and measurement precision.
Solution Approach 2:
The patent implements parameter changes by modifying the radius parameter based on statistical analysis of UE distribution. The system calculates the standard deviation of UE positions and adjusts the radius parameter accordingly, changing it from a static value to one that adapts to the specific event and crowd patterns.
2Quantity of substance
If a large Area of Interest radius is used, then more user equipment is captured, but the reliability of crowd counting deteriorates due to inclusion of non-attendees
Solution Approach 1:
The patent applies local quality by differentiating between core event attendees and peripheral users. Instead of treating all UEs within a radius equally, the system identifies a core area with high UE density corresponding to actual attendees, while excluding or weighting differently the peripheral areas that may contain non-attendees.
Solution Approach 2:
The patent uses partial action by capturing only the essential portion of the crowd data needed for accurate counting. The system determines an optimized radius that captures sufficient attendees for reliable counting without excessively including non-attendees, achieving the minimum necessary coverage for accuracy.
3Reliability
If a small Area of Interest radius is used, then the reliability of attendee identification improves, but the measurement precision deteriorates due to exclusion of actual attendees
Solution Approach 1:
The system dynamically adjusts the radius based on the specific event characteristics and observed UE distribution patterns. Rather than using a fixed small radius, the system expands or contracts the Area of Interest boundary based on real-time data, ensuring that actual attendees are included while maintaining reliability.
4Measurement precision
If the Area of Interest radius is optimized for each event, then the measurement precision of crowd estimation improves, but the device complexity increases due to additional calculations
Solution Approach 1:
The system applies self-service by automatically determining the optimal radius through statistical analysis of UE distribution data. The computation engine autonomously calculates the standard deviation and adjusts the radius parameter without requiring manual intervention, thereby improving precision while managing complexity through automation.
Solution Approach 2:
The patent implements feedback by using observed UE distribution patterns to adjust the radius parameter for subsequent calculations. The system continuously monitors the effectiveness of the current radius and refines it based on the statistical properties of the detected UEs, creating a self-improving system that balances precision and complexity.
Data Source
AI summary
A method of estimating a number of persons that gathered at an Area of Interest for attending a public happening during a time interval on a day is proposed. Said Area of Interest is defined by an Area of Interest center and an Area of Interest radius and is covered by a mobile telecommunication network having a plurality of communication stations each of which is adapted to manage communications of user equipment in one or more served areas in which the mobile telecommunication network is subdivided.