When Plausible Is Not Realistic: Evaluating Human Mobility in LLM-Based Urban Simulation
This paper introduces a validation framework to evaluate whether LLM-based urban simulators produce empirically realistic human mobility patterns or merely plausible narratives. Testing AgentSociety and CitySim on datasets from Paris and Shanghai reveals a substantial gap between narrative plausibility and empirical realism, especially in spatial and temporal constraints. The authors also provide open-source tools for scalable evaluation.
[2606.13835] When Plausible Is Not Realistic: Evaluating Human Mobility in LLM-Based Urban Simulation
[Submitted on 11 Jun 2026]
Title:When Plausible Is Not Realistic: Evaluating Human Mobility in LLM-Based Urban Simulation
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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.
Comments: 14 pages, 10 figures
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
Cite as: arXiv:2606.13835 [cs.CL]
(or arXiv:2606.13835v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.13835
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Thiago H. Silva [view email] [v1] Thu, 11 Jun 2026 19:03:52 UTC (5,971 KB)
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