Crowd Movement Visualization via Mobile Device Location Data
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Solution Overview
Problem
Current methods for monitoring crowd dynamics in large public venues are inefficient and error-prone, relying on manual security personnel and lacking effective tools to quickly detect emergencies or anomalies in crowd movement.
Innovation Solution
Collecting and analyzing location data from mobile wireless devices using a wireless network infrastructure to aggregate and visualize crowd movement patterns, identifying frequent subroutines that represent aggregate crowd behavior and alerting users to anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual security personnel monitor crowd dynamics in public venues, then security monitoring is performed, but the approach is inefficient and error-prone with slow detection of emerging situations
Solution Approach 1:
The patent replaces manual mechanical monitoring by security personnel with an automated electronic system that collects location data from mobile wireless devices, processes the data to determine crowd movement patterns, and generates alerts for emerging situations. This substitution eliminates human error and inefficiency while maintaining continuous monitoring capability.
Solution Approach 2:
The system enables self-service monitoring by utilizing the mobile devices already carried by crowd members to collect location data, rather than requiring dedicated monitoring infrastructure. The crowd essentially monitors itself through their own devices, providing accurate real-time location information for analysis.
2Quantity of substance
If current location-based analytics platforms collect and analyze data from mobile wireless devices, then data collection capability is provided, but the platforms do not efficiently monitor crowd dynamics in complex environments with large numbers of individuals
Solution Approach 1:
The patent segments the complex task of crowd monitoring into distinct processing stages: collecting individual location data points, aggregating data by mobile device to create trajectories, further segmenting trajectories into sub-routines within grid units, and analyzing patterns. This segmentation makes the complex system manageable and scalable.
Solution Approach 2:
The patent introduces spatial dimensionality by dividing the monitoring area into grid units and analyzing movement patterns within each unit. This spatial segmentation allows the system to handle complex environments by processing local patterns independently and combining them into overall crowd dynamics understanding.
3Area of stationary object
If manual monitoring methods are used to detect emerging situations, then security coverage is provided, but the detection process is slow and relies on information from bystanders
Solution Approach 1:
The system provides continuous monitoring by continuously collecting location data from mobile devices in real-time, rather than relying on intermittent manual observations or bystander reports. This continuous data stream enables immediate detection of emerging situations as they develop, significantly reducing detection time while maintaining comprehensive coverage.
Data Source
AI summary
According to the techniques presented herein, location data from signals transmitted by a plurality of mobile wireless devices in a wireless network are obtained. For each mobile wireless device, location data time points are aggregated to generate a plurality of routines or paths of movement for each mobile wireless device within a predefined space. The predefined space is partitioned into a plurality of units and each routine of the plurality of routines is also partitioned into a plurality of subroutines or segments. For each unit, one or more subroutines within a predefined distance of a frequent subroutine are combined with the frequent subroutine, and the frequent subroutines may be displayed on a graphical interface to visualize aggregate movement of the plurality of mobile wireless devices within the predefined space. Frequent subroutines may be analyzed in an automated manner to generate notifications and alerts.