A nonlinear opportunity benefit model for human mobility prediction

Human mobility patterns are fundamentally connected to urban planning, disease spread, environmental impacts, and numerous other social and ecological processes. Accurately modeling these patterns is thus essential. Traditional mobility models often assume that a location’s opportunity benefits scale linearly with its population—a simplification that fails to capture real-world dynamics. In this study, we propose the nonlinear opportunity benefit model (NOB), which introduces a nonlinear relationship between population and opportunity benefits, controlled by a tunable parameter β. Empirical analyses across multiple datasets show that intracity mobility follows a sublinear trend, while commuting and intercity travel tend to exhibit superlinear behavior. Comparative evaluations demonstrate that the NOB model consistently surpasses traditional models in predictive accuracy, offering a more nuanced understanding of human movement patterns.