Large Language Models for Intelligent Urban Digital Twins: Spatiotemporal Reasoning, Knowledge Graphs, and Simulation

Dear Colleagues,

Urban digital twins are transitioning from static 3D models to dynamic, AI-enabled systems for real-time sensing, reasoning, and decision support. The rapid advancement of large language models (LLMs) offers a paradigm shift in how urban systems are represented, interpreted, and simulated.

This Special Issue addresses the convergence of four key directions: (1) LLMs as cognitive engines for urban intelligence; (2) spatiotemporal reasoning for dynamic urban processes; (3) urban knowledge graphs for structured representation of entities and relationships; and (4) AI-driven simulation and scenario deduction for planning, governance, and emergency response.

We invite contributions on the integration of LLMs with spatiotemporal data infrastructures, the construction and scalable querying of urban knowledge graphs, and AI-augmented simulation frameworks for evidence-based urban decision-making. Both methodological innovations and applied studies are encouraged across domains such as transportation, land use, climate resilience, and public safety.

This Special Issue aims to advance the theoretical and methodological foundations of intelligent urban digital twins and accelerate their practical deployment across disciplines.

Dr. Xi Kuai
Prof. Dr. Biao He
Dr. Minmin Li
Dr. Haojia Lin
Guest Editors

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