GPS Movement Idle Segmentation for Mobile Workforce Tracking
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Solution Overview
Problem
Businesses face challenges in monitoring and managing mobile workforces that visit customer sites, as existing systems lack the ability to reliably identify movement and idle segments, leading to inefficiencies in service delivery and billing processes.
Innovation Solution
A system and method that utilizes GPS-enabled devices to convert GPS signal data into time-on-site indicator data, employing algorithms to differentiate between movement and idle modes, and integrates this data with a web-based platform for workforce management, enabling real-time tracking and data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional GPS tracking systems are used to monitor mobile workforce locations, then location data can be collected, but the system cannot reliably identify movement and idle segments
Solution Approach 1:
The patent divides continuous GPS location data into discrete segments based on movement patterns. By analyzing changes in location over time intervals, the system segments the workforce's journey into distinct movement segments (when the workforce is traveling) and idle segments (when the workforce is stationary at customer sites), enabling reliable identification of each state.
Solution Approach 2:
The system dynamically adjusts the classification of workforce status based on real-time GPS data analysis. By continuously monitoring location changes and comparing them against threshold criteria, the system adapts its determination of movement versus idle states, improving measurement precision while maintaining reliability.
2Measurement precision
If detailed GPS data collection is implemented to improve workforce tracking accuracy, then movement patterns can be identified, but system complexity increases
Solution Approach 1:
The patent extracts only the essential information needed for workforce monitoring from the complete GPS data set. By focusing on location coordinates and time stamps, and deriving movement status through algorithmic analysis rather than collecting additional data types, the system achieves high tracking accuracy without proportionally increasing system complexity.
Solution Approach 2:
The system uses the GPS device's inherent capabilities to generate the monitoring data it needs. The GPS receiver continuously provides location information, and the embedded algorithm automatically processes this data to determine movement versus idle segments, eliminating the need for separate sensors or complex external monitoring infrastructure.
3Productivity
If real-time GPS monitoring is implemented to improve workforce visibility, then productivity can be enhanced, but data processing requirements increase
Solution Approach 1:
The system performs preliminary filtering and classification of GPS data points as they are collected. By immediately analyzing each location update and pre-classifying it as movement or idle status, the system reduces the volume of raw data that requires further processing, thereby supporting real-time productivity monitoring without overwhelming data processing requirements.
Data Source
AI summary
The method converts GPS signals from a GPS-enabled phone-tablet-device into time-on-site data. A database includes unique task data and data on task person (T-P), task situs, time-on-site, and assignment. Server determines when the GPS is in an idle mode defined by a territory about a current GPS data by a threshold or other algorithm. Processor determines whether the GPS moves by applying an “idle to movement” algorithm, movement by positional data or velocity and “movement to idle.” A time-on-site is determined when idle ON and current-GPS matches task situs. Method transforms GPS data into travel time indicator data and time-on-site data for quality assurance, billing and accounting. The method automatically identifies and divides the movements of a person or apparatus into types of actions (including the non-action, idle mode). The method analyzes, identifies and divides it into sequential segments.