Significance Statement:
Human mobility models are vital for understanding urban dynamics and informing transportation, infrastructure, and public
health policies. While universal models like the Exploration and Preferential Return offer insights into population-wide movement
patterns, our research shows that aggregate accuracy can mask structured individual-level heterogeneity, with model performance varying significantly across socio-demographic groups. By analyzing large-scale mobility data, we reveal that this model
poorly represents individuals with lower incomes and more constrained routines. These systematic differences in model performance may lead to uneven predictive accuracy across socioeconomic groups when such models are used in policy-making contexts. Our findings highlight the importance of incorporating behavioral heterogeneity and urban context when interpreting
and applying universal mobility models in transportation, infrastructure, and public health.
https://academic.oup.com/pnasnexus/article/5/8/pgag267/8752539?login=false