Crowd Density Mapping with Local Measurement Nodes
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Solution Overview
Problem
Existing methods for measuring crowd density, such as camera matching photogrammetry, satellite geolocation, and cellular tower triangulation, are imprecise, leading to inaccurate assessments of crowd and traffic flow.
Innovation Solution
A system utilizing multiple local measurement nodes to define bins and regions, identify devices within these regions, create unique profiles, and calculate population density based on the number of profiles present in these regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If camera matching photogrammetry, satellite geolocation, or cellular tower triangulation is used to measure crowd density, then the measurement can be obtained, but the precision and accuracy of crowd and traffic flow assessment deteriorates
Solution Approach 1:
The system divides the monitoring area into multiple geographic bins, each further divided into several regions. Each region is associated with specific measurement nodes (WiFi access points, cellular towers, Bluetooth devices) that independently measure device presence. This segmentation allows for more precise local measurements in each region while maintaining overall system reliability through distributed data collection across multiple bins.
2Measurement precision
If multiple local measurement nodes and regions are implemented, then measurement precision improves, but device complexity increases
Solution Approach 1:
The system uses multiple types of measurement nodes (WiFi access points, cellular towers, Bluetooth devices) that serve universal functions across different geographic bins. Each node type can operate independently to identify devices and contribute to population density calculations in its associated regions, reducing the need for specialized equipment in each location and simplifying overall system deployment.
Solution Approach 2:
The system introduces geographic bins as intermediary structures that organize and manage the relationship between measurement nodes and regions. Each bin contains multiple regions and associates specific measurement nodes with those regions, creating a hierarchical intermediary layer that simplifies data aggregation and reduces the complexity of directly managing numerous individual measurement nodes across the entire monitoring area.
Data Source
AI summary
According to one embodiment, a method, computer system, and computer program product for crowd density analysis with multiple regions is provided. The embodiment may include identifying three or more local measurement nodes. The embodiment may also include defining one or more bins, each bin comprising at least three local measurement nodes from the three or more local measurement nodes. The embodiment may further include mapping one or more regions corresponding to a bin from the one or more bins. The embodiment may also include identifying a device in a region. The embodiment may further creating a profile corresponding to the identified device. The embodiment may also include calculating a population density based on a number of created profiles in a region from the one or more regions.