TravelAgent: Generative agents in the built environment
Name
noyman-et-al-2025-travelagent-generative-agents-in-the-built-environment.pdf
Description
Published version
Size
2.08 MB
Format
Adobe PDF
Checksum (MD5)
5bec36821096b2fb40bbb911ad6d06dd
Author(s)
Noyman, Ariel
Hu, Kai
Larson, Kent
Date Issued
February 2026
Journal
Environment and Planning B: Urban Analytics and City Science
Publisher
SAGE Publications
Citation
Noyman, A., Hu, K., & Larson, K. (2026). TravelAgent: Generative agents in the built environment. Environment and Planning B: Urban Analytics and City Science, 53(2), 377-397.
Version
Final published version
Abstract
Understanding human behavior in the built environment is critical for designing highly-functional, human-centered urban spaces. Traditional approaches, such as manual observations, surveys, and simple simulations, often struggle to capture the complexity and nuance of real-world human behavior and experience. Here we introduce TravelAgent, a novel agentic simulation platform that models pedestrian navigation, activity, and human-like decision-making in the built environment. TravelAgent is proposed to help design teams and decision-makers understand how different users might experience diverse built environments under varying environmental conditions. TravelAgent integrates Generative Agents, multi-modal sensory inputs, and virtual environments, enabling agents to perceive, navigate, and interact with their surroundings, with tasks ranging from goal-oriented navigation to free exploration. We share analysis from 200 simulations with 3364 decision points and task completion rate of ∼80%, across diverse spatial layouts and agent archetypes. We present spatial, linguistic, and sentiment analysis, and show how agents react and experience their surroundings. Finally, we suggest TravelAgent as a new paradigm for designing, simulating, and understanding human experiences in urban environments.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1177/23998083251360458
