Predicting Human Movement Using Location Services Model
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Solution Overview
Problem
Current technologies lack the capability to accurately predict human movement behaviors and analyze their impact on transportation systems, policy decisions, and market dynamics, as they fail to integrate comprehensive models of human interactions and affinities with real-world data effectively.
Innovation Solution
A knowledge model and location services model are developed, combining data from various sources to create a graph representing human behaviors, affinities, and relationships, which can simulate interactions with real or hypothetical networks to predict changes and analyze their effects, using cellular data, web browsing habits, and other metadata.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive models of human interactions and affinities are integrated with real-world data, then prediction accuracy of human movement behaviors is improved, but model complexity increases
Solution Approach 1:
The patent segments the complex prediction system into distinct modules: a movement behavior model that processes location data to predict physical movements, and a separate human interaction model that processes social network data to predict interaction patterns. These segmented models can be independently trained and calibrated, reducing overall system complexity while maintaining comprehensive prediction capability.
Solution Approach 2:
The patent introduces an intermediary calibration process that uses real-world observed data to adjust and align the predictions of the complex integrated model with actual human behavior patterns. This calibration layer acts as a mediator between the complex theoretical model and real-world applications, simplifying the interface between model complexity and prediction accuracy.
2Measurement precision
If multiple data sources including cellular data and web browsing habits are combined, then behavioral prediction capability is improved, but data processing complexity increases
Solution Approach 1:
The patent segments different data sources into dedicated processing streams: cellular location data is processed by the movement behavior model, while web browsing habits and social network data are processed by the human interaction model. This segmentation allows each data type to be handled by specialized algorithms, reducing overall processing complexity.
Solution Approach 2:
The patent creates a universal framework where multiple data sources feed into a common predictive architecture. The movement behavior model and human interaction model both utilize calibration processes and prediction algorithms that can handle various data types, allowing the system to process diverse inputs through unified multi-functional components.
3Reliability
If the model is calibrated with real data and used to simulate changes in transportation networks, then predictive accuracy for policy decisions is improved, but computational requirements increase
Solution Approach 1:
The patent performs preliminary calibration of the movement behavior model and human interaction model using historical real-world data before deploying the models for policy simulation. This pre-calibration step establishes accurate baseline parameters that reduce computational requirements during subsequent simulations, as the models already incorporate learned behavioral patterns.
Solution Approach 2:
The patent implements a two-tiered simulation approach where the full calibrated model is used for initial comprehensive analysis, and then simplified versions or sampled subsets of the model are used for iterative policy scenario testing. This partial action approach maintains predictive accuracy for critical decisions while reducing computational burden for routine simulations.
Data Source
AI summary
Human behavior may be predicted by building a model of people's physical movements along with a model that represents their affinities to various ontological elements, and their relationships to other people. The model may include a graph of interrelated people, as well as their hobbies, interests, employment, and other elements. The model may be analyzed by simulating people's activities as those activities interact with real or hypothetical networks. The real networks may be used to verify the model by comparing measured parameters to predictive parameters derived from simulation to calibrate the models. A calibrated model may then be used with a modified or hypothetical network to analyze the effects of changes to the real network.