mlsanityData to 14 Oct 2026

Deep Generative Model for Human Mobility Behavior

arXiv 2510.06473 ↗First posted 7 Oct 2025physics.soc-ph cs.AI cs.SIcited by 0

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Abstract (arXiv)

Understanding and modeling human mobility is central to challenges in transport planning, sustainable urban design, and public health. Despite decades of effort, simulating individual mobility remains challenging because of its complex, context-dependent, and exploratory nature. Here, building on the activity-based view of daily mobility, we propose MobilityGen, a diffusion-based generative framework for simulating multi-attribute activity-travel sequences over days to weeks at large spatial scales. By linking behavioral attributes with environmental context, MobilityGen reproduces key patterns such as scaling laws for location visits, activity time allocation, and the coupled evolution of travel mode and destination choices. It reflects spatio-temporal variability and generates diverse and plausible mobility patterns consistent with the built environment. Beyond standard validation, MobilityGen enables analyses that have been difficult with earlier models, including how access to urban space varies across travel modes and how co-presence dynamics shape social exposure and segregation. Together, these results support an integrated, data-driven basis for fine-grained studies of human mobility behavior and its societal implications.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 28 of its 79 references that are in the map. First posted on arXiv 7 Oct 2025.

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28 of 79 references.

References · 28

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16 more
  1. 2020Multi-Scale Representation Learning for Spatial Feature Distributions using Grid Cellscited by 90
  2. 2020A Survey on Deep Learning for Human Mobilitycited by 88
  3. 2020Deep Gravity: enhancing mobility flows generation with deep neural networks and geographic informationcited by 58
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  9. 2024Future Directions in Human Mobility Sciencecited by 16
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  12. 2016Understanding Predictability and Exploration in Human Mobilitycited by 7
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  1. 2024Noise Matters: Diffusion Model-based Urban Mobility Generation with Collaborative Noise Priors
  2. 2026When Plausible Is Not Realistic: Evaluating Human Mobility in LLM-Based Urban Simulation
  3. 2024MobilityGPT: Enhanced Human Mobility Modeling with a GPT model
  4. 2025Dynamic Population Distribution Aware Human Trajectory Generation with Diffusion Model
  5. 2025MobileCity: An Efficient Framework for Large-Scale Urban Behavior Simulation
  6. 2025GATSim: Urban Mobility Simulation with Generative Agents
  7. 2026ActivityEditor: Learning to Synthesize Physically Valid Human Mobility
  8. 2026MobiDiff: Semantic-Aware Multi-Channel Discrete Diffusion for Human Mobility Data Generation