Navigating Heat Exposure: Simulation of Route Planning Based on Visual Language Model Agents

Haoran Ma; Kaihan Zhang; Jiannan Cai

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arXiv · 2503.12731

Navigating Heat Exposure: Simulation of Route Planning Based on Visual Language Model Agents

Haoran Ma·Kaihan Zhang·Jiannan Cai

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Abstract

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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BibTeX RIS

Haoran Ma, Kaihan Zhang, Jiannan Cai. 2025-03-17. Navigating Heat Exposure: Simulation of Route Planning Based on Visual Language Model Agents. https://arxiv.org/abs/2503.12731

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