用AI模拟不同人群在高温下的步行路线选择，更贴近真实行为。
Navigating Heat Exposure: Simulation of Route Planning Based on Visual Language Model Agents
- 基于视觉语言模型构建八类耐热性格画像，模拟个体差异。
- 模拟结果与问卷数据高度一致，准确捕捉不同人群决策差异。
- 成本仅0.006美元/条路径，适合大规模城市气候规划应用。
高温显著影响行人路径选择行为。现有方法如基于代理的建模（ABM）和实证测量无法反映个体生理差异及热应激下的环境感知机制，导致缺乏以人为本的热适应路线建议。为此，我们提出一种由视觉语言模型（VLM）驱动的“人格-感知-规划-记忆”（PPPM）框架，融合街景图像与城市路网拓扑，模拟热适应性步行路径。通过在Gemini-2.0模型上进行结构化提示工程，构建了八种不同的热敏感人格类型，以模拟高温下移动行为，并通过问卷调查进行实证验证。结果表明，模拟输出能有效捕捉人格间差异，与实际观测路线偏好具有高度一致性，凸显了影响个体决策的关键因素差异。该框架成本极低，单条路径模拟仅需0.006美元、耗时47.81秒。此项人工智能生成内容（AIGC）方法推动了城市气候适应研究，实现了热响应型出行模式的高分辨率模拟，为气候韧性城市规划提供可行动洞察。
原文摘要 · Abstract (English)
Heat exposure significantly influences pedestrian routing behaviors. Existing methods such as agent-based modeling (ABM) and empirical measurements fail to account for individual physiological variations and environmental perception mechanisms under thermal stress. This results in a lack of human-centred, heat-adaptive routing suggestions. To address these limitations, we propose a novel Vision Language Model (VLM)-driven Persona-Perception-Planning-Memory (PPPM) framework that integrating street view imagery and urban network topology to simulate heat-adaptive pedestrian routing. Through structured prompt engineering on Gemini-2.0 model, eight distinct heat-sensitive personas were created to model mobility behaviors during heat exposure, with empirical validation through questionnaire survey. Results demonstrate that simulation outputs effectively capture inter-persona variations, achieving high significant congruence with observed route preferences and highlighting differences in the factors driving agents decisions. Our framework is highly cost-effective, with simulations costing 0.006USD and taking 47.81s per route. This Artificial Intelligence-Generated Content (AIGC) methodology advances urban climate adaptation research by enabling high-resolution simulation of thermal-responsive mobility patterns, providing actionable insights for climate-resilient urban planning.
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