Generative Agents for High-Fidelity Simulation of Community-Scale Mobility and Energy Behavior
コミュニティ規模のモビリティとエネルギー行動の高忠実度シミュレーションのための生成エージェント (AI 翻訳)
Yongjian Chen, Zhi Yang, Shiqi Ou
🤖 gxceed AI 要約
日本語
本論文は、大規模言語モデルを用いて人間の多様な行動特性を再現する生成エージェントベースのシミュレーションフレームワーク（HGA-Sim）を提案。495エージェントのコミュニティ検証により、実際のモビリティとエネルギー消費パターンを高い精度で再現（RMSE 0.1983）。持続可能なモビリティ設計や新政策の社会経済的影響評価に貢献。
English
This paper proposes the Hierarchical Generative Agent-based Simulation Framework (HGA-Sim) that uses large language models to autonomously generate agents with diverse personality traits for realistic simulation of human behavior in mobility and energy consumption. Validated on a 495-agent community, it reproduces aggregate patterns with high fidelity (RMSE 0.1983). It provides a virtual testing environment for evaluating sustainable mobility designs and energy policies, reducing risks in infrastructure investments.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本のGX文脈では、スマートシティや地域エネルギーマネジメントの政策設計に応用可能。本フレームワークにより、住民行動を考慮した現実的なシミュレーションが実現し、SSBJや統合報告書における非財務情報の裏付けとしても活用可能性がある。
In the global GX context
Globally, this framework addresses a critical gap in energy transition modeling by capturing human behavioral heterogeneity. It supports evidence-based policy design for sustainable mobility and energy systems, relevant to ISSB, TCFD, and transition finance assessments where human behavior impacts decarbonization pathways.
👥 読者別の含意
🔬研究者:Novel integration of LLMs with agent-based modeling for socio-technical simulation, offering a scalable method for studying human behavior in energy transitions.
🏢実務担当者:Can use the framework to simulate community responses to new mobility services or energy policies, aiding investment and operational decisions.
🏛政策担当者:Provides a tool to ex-ante evaluate the socio-economic impacts of transportation and energy policies, reducing uncertainty in policy design.
📄 Abstract（原文）
<div class="section abstract"><div class="htmlview paragraph">The transition to sustainable mobility and energy systems represents a complex socio-technical challenge, with the success of new technologies and policies critically dependent on their interaction with human behavior. Traditional models frequently struggle to capture the nuanced, heterogeneous, and adaptive characteristics of individual decision-making in mobility choices and energy usage, thereby introducing significant uncertainties into system design and policy evaluation. This paper presents a novel paradigm to bridge this gap: the Hierarchical Generative Agent-based Simulation Framework (HGA-Sim). The framework's core innovations are twofold: 1) It utilizes Large Language Models to generate agents endowed with intrinsic personality traits autonomously, enabling a realistic simulation of diverse, human-like responses to environmental stimuli and personal experiences. 2) It employs a hierarchical "Archetype -Individual" architecture, rendering large-scale community simulations computationally feasible. Validated through a case study of a 495-agent community, the HGA-Sim framework accurately reproduces aggregate mobility and energy consumption patterns, including critical peak loads and temporal dynamics. It demonstrates remarkable fidelity to real-world data with a Root Mean Square Error of 0.1983. By establishing a human-in-the-loop virtual testing environment, this research provides a foundational tool for evaluating the real-world viability of sustainable mobility designs, assessing the potential socioeconomic impacts of new transportation and energy policies, and mitigating risks associated with investments in future sustainable infrastructure.</div></div>
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.4271/2026-01-0465first seen 2026-05-17 06:58:01 · last seen 2026-05-20 05:15:29
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