Deep Generative Model for Human Mobility Behavior

MobilityGen, a diffusion-based generative framework, has been introduced for simulating multi-attribute activity-travel sequences over extended periods and large spatial scales. It effectively links behavioral attributes with environmental context, capturing key patterns such as scaling laws for location visits and the evolution of travel modes and destination choices. This model enhances the capability for nuanced analyses of human mobility behavior, offering insights into urban space access and social dynamics, which are critical for transport planning and urban design.