mlsanityData to 14 Oct 2026

Encounter-Driven City-Scale Human Mobility Generation with Large Language Models

arXiv 2610.17672 ↗First posted 13 Oct 2026cs.AIcited by 0

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Abstract (arXiv)

Large language models (LLMs) have shown strong potential for human mobility generation by modeling individual routines, activities, and mobility preferences. However, existing approaches remain largely resident-centric: generating realistic individual trajectories does not necessarily recover the encounters that emerge when multiple residents co-occur at the same place and time. To this end, we study encounter-aware human mobility generation, which jointly preserves individual mobility and population-level interaction structure. We propose EncounterMob, an encounter-driven LLM framework consisting of a coarse-to-fine Encounter Generator and an Iterative Coupling Framework. The Encounter Generator first captures city-level encounter rhythms and then instantiates participant-time-venue encounters, while the Iterative Coupling Framework coordinates these encounters with personal mobility behavior through successive refinement. We further introduce an encounter recovery protocol covering encounter marginal and joint distributions over residents, times, and venues. Experiments on the Japan and NYC datasets show that EncounterMob outperforms all external baselines on all seven encounter metrics and all six personal mobility metrics. Compared with ELLMob, it reduces the participant-time-venue joint error by 40.4%/41.7% and the mean personal mobility error by 58.8%/32.7% on Japan/NYC, respectively.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 35 references that are in the map. First posted on arXiv 13 Oct 2026.

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16 of 35 references.

References · 16

  1. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,442
  2. 2023CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquingcited by 783
  3. 2024Large Language Models as Urban Residents: An LLM Agent Framework for Personal Mobility Generationcited by 100
  4. 2023Where Would I Go Next? Large Language Models as Human Mobility Predictorscited by 93
  5. 2020A Survey on Deep Learning for Human Mobilitycited by 88
  6. 2023GETNext: Trajectory Flow Map Enhanced Transformer for Next POI Recommendationcited by 58
  7. 2020TrajGAIL: Generating Urban Vehicle Trajectories using Generative Adversarial Imitation Learningcited by 47
  8. 2024Mobility-LLM: Learning Visiting Intentions and Travel Preferences from Human Mobility Data with Large Language Modelscited by 22
  9. 2025TrajLLM: A Modular LLM-Enhanced Agent-Based Framework for Realistic Human Trajectory Simulationcited by 22
  10. 2022Context-aware multi-head self-attentional neural network model for next location predictioncited by 21
  11. 2024Noise Matters: Diffusion Model-based Urban Mobility Generation with Collaborative Noise Priorscited by 8
  12. 2013Understanding metropolitan patterns of daily encounterscited by 7
4 more
  1. 2022Generating synthetic mobility data for a realistic population with RNNs to improve utility and privacycited by 7
  2. 2024Human Mobility Modeling with Household Coordination Activities under Limited Information via Retrieval-Augmented LLMscited by 7
  3. 2025WorldMove, a global open data for human mobilitycited by 4
  4. 2026ELLMob: Event-Driven Human Mobility Generation with Self-Aligned LLM Frameworkcited by 4

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