When Plausible Is Not Realistic: Evaluating Human Mobility in LLM-Based Urban Simulation
Quick Answer
This study evaluates LLM-based urban simulators like AgentSociety and CitySim, revealing a significant gap between narrative plausibility and real-world mobility realism.
Quick Take
This study evaluates LLM-based urban simulators like AgentSociety and CitySim, revealing a significant gap between narrative plausibility and real-world mobility realism. Using datasets from Greater Paris and Shanghai, the analysis shows these models struggle with core spatial and temporal constraints, necessitating rigorous empirical validation and improved initialization methods for realistic urban simulations.
Key Points
- Introduces a validation framework for evaluating mobility realism in LLM-based simulators.
- Finds substantial gaps in trip-length distributions and origin-destination flows.
- Highlights instability in realistic mobility diversity across prompting configurations.
- Provides scalable infrastructure for map generation and mobility-metric computation.
- Emphasizes the need for empirical validation in urban simulation systems.
Paper Resources
Article Content
From source RSS / original summaryarXiv:2606. 13835v1 Announce Type: new Abstract: LLM-based generative agents are increasingly used in urban simulators, yet it remains unclear whether they reproduce empirically realistic human mobility patterns or merely generate plausible mobility narratives. We introduce a validation framework for evaluating the mobility of generative agents of LLM-based urban simulators against real-world mobility data.
For this, we use mobility laws, temporal rhythms, network motifs, semantic activity transitions, and behavioral mobility profiles. Using datasets from the Greater Paris region and Shanghai, we evaluate AgentSociety and CitySim across multiple dimensions of mobility realism. Our analysis reveals a substantial gap between narrative plausibility and empirical mobility realism.
Although the simulators capture some high-level semantic activity distributions, they struggle to reproduce core spatial and temporal constraints, including realistic trip-length distributions, origin-destination flows, dwell times, and transition dynamics. We further observe that realistic mobility diversity is unstable across default prompting configurations and may require explicit profile-aware initialization.
To support reproducible evaluation, we also contribute scalable and open LLM-driven infrastructure for regional-scale map generation, observability-enhanced simulation, mobility-metric computation, and traffic simulation. Our findings highlight the need for rigorous empirical validation of LLM-based urban simulators and provide practical tools for building more realistic and reproducible urban simulation systems.
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