Deep Generative Model for Human Mobility Behavior
This work addresses the problem of modeling complex human mobility for applications in transport planning, urban design, and public health, offering a new framework for fine-grained, data-driven studies.
The authors tackled the challenge of simulating individual human mobility by developing MobilityGen, a deep generative model that produces realistic mobility trajectories at large scales, reproducing key patterns like scaling laws for location visits and activity time allocation.
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, we present MobilityGen, a deep generative model that produces realistic mobility trajectories spanning 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, plausible, and novel mobility patterns consistent with the built environment. Beyond standard validation, MobilityGen yields insights not attainable with earlier models, including how access to urban space varies across travel modes and how co-presence dynamics shape social exposure and segregation. Our work establishes a new framework for mobility simulation, paving the way for fine-grained, data-driven studies of human behavior and its societal implications.