arXiv:2510.06473v3 Announce Type: replace-cross Abstract: 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
Advances in AI, particularly diffusion models, are enabling sophisticated simulations of complex human behaviors previously intractable, coinciding with increased computational power.
Precise modeling of human mobility is critical for urban planning, public health, and transportation, allowing for proactive interventions and optimized resource allocation in increasingly complex societies.
The ability to simulate individual multi-attribute activity sequences over extended periods at large scales marks a significant leap in predictive behavioral modeling, moving beyond aggregate trends.
- · Urban Planners
- · Public Health Authorities
- · Logistics Companies
- · Smart City Developers
- · Inefficient Transportation Systems
- · Static Urban Planning Methodologies
Improved urban infrastructure and service planning based on more accurate predictive models of human movement and activity.
Development of adaptive city systems that can dynamically respond to predicted mobility patterns, reducing congestion and resource strain.
Ethical and privacy debates intensify regarding the use of highly detailed generative models for simulating and potentially influencing human behavior at scale.
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Read at arXiv cs.AI