Mobility Data Processing for Social Group Identification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current technologies face challenges in identifying mobility groups based on social relationships from mobility data, as existing methods struggle to distinguish groups moving together due to dynamic changes in social configurations, making it difficult to accurately determine movement context and provide effective guidance or advertisements.
Innovation Solution
A mobility data processing system that includes a management server with various processing units to analyze mobility data, identify group possibilities, and determine group relationships by using weight-based algorithms and data management tables to assess movement trajectories and context, enabling accurate group identification and targeted guidance or advertisements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If grouping is performed based on position, time and speed information only, then statistical population groups can be identified by region, but groups based on social relationships cannot be distinguished
Solution Approach 1:
The patent applies dynamics by making the grouping criteria adaptable and changeable over time. The system dynamically adjusts grouping parameters based on multiple factors including position, time, speed, and importantly, social relationship data. This allows the system to transition from static regional grouping to dynamic social relationship-based grouping, resolving the contradiction between measurement precision and adaptability.
Solution Approach 2:
The patent changes the parameters used for grouping from basic movement parameters (position, time, speed) to include social relationship parameters. By introducing new parameters such as social relationship strength, interaction frequency, and relationship type, the system can accurately identify both statistical populations and social groups, simultaneously improving measurement precision and adaptability.
2Productivity
If guidance is performed for each individual person, then individual mobility can be managed, but group characteristics and social relationships are lost
Solution Approach 1:
The patent merges individual mobility data with social relationship information to create comprehensive group profiles. By combining individual position, speed, and time data with social relationship parameters, the system can perform guidance at both individual and group levels simultaneously, maintaining productivity while preserving social relationship information.
Solution Approach 2:
The patent introduces social relationship data as an intermediary layer between individual mobility data and group guidance outcomes. This intermediary allows the system to aggregate individual data into meaningful social groups while retaining the ability to access and utilize social relationship characteristics for optimized guidance strategies.
3Quantity of substance
If mobility data is collected from individual mobile terminals, then individual position information can be obtained, but dynamic social configurations cannot be identified
Solution Approach 1:
The patent implements feedback mechanisms where social relationship information is continuously updated based on observed mobility patterns. The system uses feedback from individual terminal data to infer social relationships, and then uses this inferred social structure to refine grouping accuracy, creating a closed-loop system that improves measurement precision while utilizing available data quantity.
Solution Approach 2:
The patent performs preliminary analysis of mobility patterns to identify potential social relationships before formal grouping occurs. By pre-processing individual terminal data to detect co-movement patterns, interaction frequencies, and temporal correlations, the system prepares social relationship information in advance, enabling accurate group identification without requiring additional data collection infrastructure.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A management server 101 is adapted to be provided with: a field data managing unit 113 configured to store mobility data of mobility instances; a group possibility degree determining unit 151 configured to execute a predetermined process for improving identification of mobility context, for the mobility data; and a group extracting unit 152 configured to, based on a time distance and a spatial distance between mobility data of one mobility instance and mobility data of another mobility instance, identify mobility context of the one mobility instance after the process is performed.