Pedestrian Movement Simulation Using Synthetic Agents
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Solution Overview
Problem
Current methods for simulating pedestrian movement in districts face challenges in balancing accuracy with privacy concerns, as they often rely on individual data modeling, which can be invasive. Additionally, existing approaches struggle to predict congestion levels effectively without compromising user privacy.
Innovation Solution
A method and system for simulating pedestrian movement in districts using a hardware processor to generate and distribute simulated users based on district plans, applying gravity models and distribution functions to predict walking paths and congestion levels, while maintaining user anonymity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If individual data modeling approaches are used to simulate pedestrian movement, then prediction accuracy is improved, but user privacy is compromised
Solution Approach 1:
The patent creates synthetic pedestrian agents that copy aggregate movement patterns and behaviors observed in real data without using actual individual trajectories. These synthetic agents reproduce statistical characteristics of pedestrian flows while being completely fictitious, thus maintaining prediction accuracy while eliminating privacy risks associated with using real individual data
Solution Approach 2:
The patent extracts only the essential statistical patterns and aggregate flow characteristics from real movement data, separating these useful predictive features from the sensitive individual-level information. By taking out only the necessary aggregate patterns and discarding individual identifiers and specific trajectories, the system maintains accuracy while protecting privacy
2Measurement precision
If detailed individual trajectory data is collected to predict congestion levels, then congestion prediction accuracy is improved, but data storage and processing complexity increases
Solution Approach 1:
The patent segments the pedestrian population into multiple synthetic agents distributed across the district, each following simple movement rules based on local conditions. This segmentation allows the system to predict congestion by analyzing the collective behavior of many simple agents rather than processing complex individual trajectory data, reducing computational complexity while maintaining accuracy
Solution Approach 2:
The synthetic pedestrian agents autonomously determine their own movement paths and behaviors based on predefined rules and local environmental conditions, without requiring centralized control or complex processing of individual data. Each agent independently contributes to the overall congestion pattern, allowing the system to emerge accurate predictions from simple local interactions
Data Source
AI summary
Methods, systems, and media for simulating movement data of pedestrians in a district are provided. The method comprises: receiving a district plan for a proposed district; determining a number of users that would populate a building in the proposed district; generating a population of simulated users based on the determined number of users that would populate the building in the proposed district; assigning, for the population of simulated users in the building, groups of simulated users in the population of simulated users to a movement category from a plurality of movement categories; distributing, for the groups of simulated users assigned to each of the plurality of movement categories, a group of simulated users to a first destination within the proposed district that satisfies the movement category by determining a probable walking path from the building to the first destination; in response to determining that the first destination within the proposed district has reached a first capacity threshold, distributing remaining users from the group of simulated users to a second destination within the proposed district that satisfies the movement category until at least one of the remaining users in the movement category has been distributed and the second destination within the proposed district has reached a second capacity threshold; distributing, for the groups of simulated users assigned to each of the plurality of movement categories, trips having the probable walking path of the group of simulated users over time by applying a distribution function to each of the plurality of movement categories; and causing a map representation of the proposed district to be presented, wherein the map representation highlights each of the trips having the probable walking path distributed over time to indicate predicted levels of congestion at a particular time in the proposed district.