# When Plausible Is Not Realistic: Evaluating Human Mobility in LLM-Based Urban Simulation

arXiv: https://arxiv.org/abs/2606.13835 · HTML: https://mlsanity.com/paper/2606.13835/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use.
- Builds on 9 of its 45 references that are in the map.
- First posted on arXiv 11 Jun 2026.

## Placed in

- LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/)

## Builds on (most cited first, 9 of 9)

- [Large Language Models as Urban Residents: An LLM Agent Framework for Personal Mobility Generation](https://mlsanity.com/paper/2402.14744/) (2024)
- [Exploring Large Language Models for Human Mobility Prediction under Public Events](https://mlsanity.com/paper/2311.17351/) (2023)
- [Do Large Language Models Solve the Problems of Agent-Based Modeling? A Critical Review of Generative Social Simulations](https://mlsanity.com/paper/2504.03274/) (2025)
- [CitySim: Modeling Urban Behaviors and City Dynamics with Large-Scale LLM-Driven Agent Simulation](https://mlsanity.com/paper/2506.21805/) (2025)
- [Understanding Predictability and Exploration in Human Mobility](https://mlsanity.com/paper/1608.01939/) (2016)
- [The NetMob25 Dataset: A High-resolution Multi-layered View of Individual Mobility in Greater Paris Region](https://mlsanity.com/paper/2506.05903/) (2025)
- [LLMs for Human Mobility: Opportunities, Challenges, and Future Directions](https://mlsanity.com/paper/2603.12420/) (2026)
- [CrimeMind: Simulating Urban Crime with Multi-Modal LLM Agents](https://mlsanity.com/paper/2506.05981/) (2025)
- [Zen: LSTM-based generation of individual spatiotemporal cellular traffic with interactions](https://mlsanity.com/paper/2301.02059/) (2023)

## Built on by (most cited first, 2 of 2)

- [CityBehavEx: A Scalable and Empirically Validated LLM-Assisted Urban Simulation Platform](https://mlsanity.com/paper/2607.12086/) (2026)
- [CALM: A Calibrated LLM Choice Network Framework for Activity-Based Traveler Simulation](https://mlsanity.com/paper/2609.22252/) (2026)

## Abstract (arXiv)

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.