Deep Generative Model for Human Mobility Behavior

2025-10-07

AI Summary

Human mobility modeling has long suffered from insufficient representation of complexity, context dependence, and underlying social mechanisms. To address this, we propose MobilityGen—the first deep generative model that jointly incorporates multimodal travel behavior, heterogeneous urban spatial accessibility, and dynamic co-occurrence patterns. It synthesizes realistic individual trajectories spanning days to weeks, simultaneously capturing the coupled evolution of spatiotemporal regularities, activity allocation, transportation mode choice, and destination selection. Unlike conventional models, MobilityGen uniquely enables interpretable modeling of social exposure and spatial segregation mechanisms. By preserving trajectory plausibility and diversity, it overcomes the longstanding trade-off between generative fidelity and sociological insight. Empirical evaluation demonstrates state-of-the-art performance in both trajectory reconstruction and social mechanism discovery. This work establishes a new paradigm for applications in urban transportation planning, equitable city design, and public health analytics.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Generation

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Web Mining and Content Analysis: Web data generation and simulation

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, 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.

Problem

• Simulating complex individual mobility patterns at scale
• Linking behavioral attributes with environmental context
• Generating realistic trajectories for urban planning applications

Innovation

• Deep generative model for realistic mobility trajectories
• Links behavioral attributes with environmental context
• Generates diverse mobility patterns consistent with built environment