PlaceSim: An LLM-based Interactive Platform for Human Behavior Simulation in Physical Facilities

Lee, S., Y. Yu, D. Shin, & R. Singh

Proceedings of the 34th ACM International Conference on Information and Knowledge Management (CIKM '25)

November 10-14, 2025, Seoul, Republic of Korea

Forthcoming

Abstract

An innovative interactive platform that leverages Large Language Models to simulate human behavior in physical facilities. This project combines AI, spatial analytics, and behavioral modeling to create realistic simulations for urban planning and facility design. The platform enables researchers and practitioners to understand and predict human movement patterns and spatial interactions in various built environments.

Research Motivation

🎯 Problem Statement

Traditional human behavior simulation in physical spaces relies on simplified agent-based models that fail to capture the complexity of real human decision-making processes. Current approaches struggle with:

Limited contextual understanding of spatial environments
Inability to model complex social interactions and preferences
Lack of adaptability to diverse facility types and user demographics

🚀 Research Opportunity

Large Language Models present an unprecedented opportunity to create more realistic and nuanced simulations by:

Leveraging natural language understanding for complex behavioral reasoning
Incorporating rich contextual information about spaces and user intentions
Enabling dynamic adaptation to different scenarios and environments

Methodology

🏗️ Platform Architecture

PlaceSim integrates multiple components to create a comprehensive simulation environment:

LLM-based Reasoning Engine: Core decision-making component for agent behavior
Spatial Analytics Module: Processing and understanding of physical facility layouts
Behavioral Modeling Framework: Integration of psychological and sociological factors
Interactive Visualization: Real-time simulation display and control interface

🔬 Technical Implementation

Multi-Agent System: Each simulated person as an independent LLM-powered agent
Prompt Engineering: Specialized prompts for spatial reasoning and behavior prediction
Environmental Context Integration: Real-time facility information processing
Validation Framework: Comparison with real-world behavioral data

Key Findings

📊

Enhanced Realism

LLM-based agents demonstrate significantly more realistic behavior patterns compared to traditional rule-based simulations.

🎯

Contextual Adaptability

The platform successfully adapts to different facility types (offices, malls, museums) with minimal parameter adjustments.

⚡

Interactive Insights

Real-time simulation enables immediate feedback for facility design decisions and urban planning scenarios.

🔄

Scalable Framework

The modular architecture allows for easy extension to new environments and behavioral factors.

Applications & Impact

🏢 Urban Planning

Optimize building layouts and public space design based on predicted human flow patterns.

🏪 Retail Design

Improve store layouts and customer experience through behavior simulation.

🚨 Emergency Planning

Test evacuation procedures and safety protocols in virtual environments.

📚 Research Tool

Enable researchers to study human-space interactions at scale.

Technical Specifications

Large Language Models Simulation Modeling Spatial Analytics Multi-Agent Systems Interactive Visualization Behavioral Psychology Urban Planning