Population Tracking via Mesh Cell Interpolation
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Solution Overview
Problem
Accurately tracking and counting population movement using mobile network operational data is challenging due to limited location information and low frequency of data transmission, which hinders precise urban planning, traffic management, and disaster prevention efforts.
Innovation Solution
The method involves receiving mobile phone operational data, filtering it based on time and area, and using interpolation algorithms to estimate intermediate positions of user equipment trajectories, employing shortest path estimation algorithms that consider geographic information and probabilities to count individuals in specific areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If sector-level location information from mobile network operational data is used, then population tracking can be performed with lower messaging overhead and less battery consumption, but location accuracy deteriorates due to large sector sizes ranging from hundreds of meters to kilometers
Solution Approach 1:
The patent divides a sector into multiple mesh cells (e.g., 3x3 grid) to provide finer location granularity. Each mesh cell represents a smaller geographic area within the sector, allowing more precise location estimation without requiring more frequent signaling. This segmentation enables the system to track users at a resolution level that balances accuracy with the low-frequency nature of operational data transmission.
Solution Approach 2:
The patent transitions from tracking users at the sector level (coarse granularity) to mesh cell level (fine granularity) by introducing an intermediate spatial dimension. This dimensional refinement allows the system to achieve GPS-like location precision using only the low-frequency operational data, without requiring users to transmit high-frequency location updates.
2Loss of information
If periodic location update messages with longer time intervals are used, then messaging overhead and battery consumption are reduced, but tracking accuracy deteriorates due to limited temporal resolution
Solution Approach 1:
The patent pre-calculates and stores mesh cell trajectories by analyzing historical movement patterns and applying shortest path algorithms on geographic information. These pre-computed trajectories enable the system to accurately estimate user locations at intermediate time points between operational data transmissions, effectively filling temporal gaps without requiring more frequent messaging.
Solution Approach 2:
The patent creates virtual trajectory copies by interpolating user movement paths between observed location points. Instead of requiring continuous real-time location data, the system generates probable trajectory copies based on historical patterns and geographic constraints, allowing accurate population tracking at high temporal resolution from low-frequency operational data.
3Measurement precision
If GPS-level accurate location information is required, then location precision is improved, but the complexity of the system increases due to need for signal processing techniques and additional data requirements
Solution Approach 1:
The patent introduces mesh cells as an intermediary spatial unit between sectors and GPS coordinates. Instead of directly mapping users to GPS-level precision using complex signal processing, the system uses mesh cells as intermediate targets that can be accurately estimated from operational data. This intermediary layer simplifies the overall system architecture while achieving sufficient location precision for population tracking applications.
Data Source
AI summary
Methods and apparatuses are disclosed herein for population tracking, counting and/or movement estimation. In one embodiment, the method comprises receiving mobile phone operational data indicative of user equipment location, where the event data includes location area update messages and periodic registration messages; and performing travel estimation based on the mobile phone operation data, including performing interpolation on data associated with one or more individuals in a population to estimate intermediate positions of a trajectory of each of the one or more individuals for a specified time period based on a shortest path mesh sequence estimation algorithm.