Crowd Flow Prediction Using Behavior Intention Simulation
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Solution Overview
Problem
Traditional agent-based simulations and data assimilation methods fail to accurately predict future crowd flows, as they are limited to scenario analysis and cannot account for actual future events, and current data assimilation methods only track individual positions without effectively predicting crowd behavior.
Innovation Solution
A prediction system that simulates crowd flows based on multiple behavior intentions of individuals, evaluates these simulations using observation data, and estimates behavior intentions to predict future crowd movements, improving accuracy through iterative simulation and evaluation processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional agent-based simulation is used to model individual movements, then scenario analysis can be performed, but future crowd flow prediction accuracy is insufficient
Solution Approach 1:
The patent implements feedback by comparing simulation results with actual observation data and using this comparison to correct and update behavior intention parameters. The system continuously refines its predictions by feeding back discrepancies between simulated and observed crowd flows, thereby improving prediction accuracy while maintaining manageable simulation complexity through targeted parameter adjustment.
Solution Approach 2:
The patent changes parameters by dynamically adjusting behavior intention parameters based on observation data. Instead of fixing individual movement parameters, the system modifies aggregate behavior parameters to match observed patterns, enabling accurate future flow prediction without requiring complex individual-level simulation for every scenario.
2Measurement precision
If data assimilation method is used to track individual positions, then observation data can be incorporated, but crowd behavior prediction remains ineffective
Solution Approach 1:
The patent transitions from tracking individual positions in spatial dimensions to analyzing aggregate behavior patterns in temporal and behavioral dimensions. By shifting focus from where individuals are to how crowds behave collectively over time, the system achieves effective crowd behavior prediction while still incorporating precise observation data as a foundation for understanding behavioral patterns.
3Reliability
If simulation based on multiple behavior intentions is performed, then prediction accuracy can be improved, but computational load increases
Solution Approach 1:
The patent applies partial action by focusing computational resources on simulating and adjusting the most influential behavior intention parameters rather than exhaustively modeling every possible individual behavior. This selective approach maintains high prediction accuracy by concentrating computational effort on key behavioral drivers while reducing overall computation time.
Data Source
AI summary
A non-transitory computer-readable storage medium storing a prediction program that causes at least one computer to execute a process, the process includes simulating a flow of people based on a plurality of behavior intentions of people; evaluating the simulated flow based on observation data of the flow of people; and predicting the flow of people based on an evaluation result of the simulated flow of people.