Learning the complexity of urban mobility with deep generative network.

Abstract

City-scale individual movements, population flows, and urban morphology are intricately intertwined, collectively contributing to the complexity of urban mobility and impacting critical aspects of a city, from socioeconomic exchanges to epidemic transmission. Existing models, derived from fundamental laws of human mobility, often capture only partial facets of this complexity. This article introduces DeepMobility, a powerful deep generative collaboration network designed to encapsulate the multifaceted nature of complex urban mobility within one unified model, bridging the gap between the heterogeneous behaviors of individuals and the collective behaviors emerging from the entire population. As the first generative deep learning model to integrate micro- and macrolevel dynamics through bidirectional collaboration, DeepMobility generates high-fidelity synthetic mobility data, overcoming key limitations of prior approaches. Our experiments, conducted on mobility trajectories and flows in cities of China and Senegal, reveal that unlike state-of-the-art deep learning models that tend to "memorize" observed data, DeepMobility excels in learning the intricate data distribution and successfully reproduces the existing universal scaling laws that characterize human mobility behaviors at both individual and population levels. DeepMobility also exhibits robust generalization capabilities, enabling it to generate realistic trajectories and flows for cities lacking corresponding training data. Our approach underscores the feasibility of employing generative deep learning to model the underlying mechanism of human mobility and establishes a versatile framework for mobility data generation that supports sustainable and livable cities.