Unraveling metro mobility patterns in China: A multi-city comparative study using travel motifs and entropy analysis

Human mobility varies significantly across temporal and spatial scales and exhibits distinct characteristics. However, precise methods and metrics for measuring human mobility are still lacking and the underlying mechanisms across different spatiotemporal scales and social groups remain unexplored. To uncover the regularity of urban travel patterns, we propose statistical methods focused on different types of entropy values. We introduce the concepts of mobility chains and travel motifs to provide diversified perspectives to observing users’ travel behaviors, choices, preferences and other characteristics. Our findings reveal that users with the same travel motifs share similar proportions in each city and follow consistent travel regularities within their motif categories. Given the importance of understanding human mobility, we emphasize the need for quantitative models that account for the statistical characteristics of individual human trajectories. To address this, we introduce the Preferential Return (PR) model, which explains the observed scaling laws and analytically simulates users’ travel behaviors. Our model and associated rules establish an underlying mechanism at the individual level, capable of explaining a variety of human mobility behaviors with different travel characteristics. These analyses and explanations have significant applications in reproducing human movement patterns. Our study provides a scientific basis for understanding and optimizing urban traffic management, enhancing public service efficiency, and promoting sustainable urban development. We believe these insights will contribute to the harmonious progress of society. • Uncover metro mobility patterns across spatiotemporal scales and social groups. • Users with the same travel motif share similar proportions and travel patterns. • Simulations explain observed scaling laws and reproduces users’ travel behaviors.