Accurately modeling of individual movement in cities has significant implications for policy decisions across various sectors. Existing research emphasizes the universality of human mobility, positing that simple models can accurately capture population-level movements. However, population-level accuracy can mask structured heterogeneity at the individual level and does not guarantee consistent performance across all individuals. By overlooking individual differences, universality laws may accurately describe certain groups while less precisely representing others, resulting in systematic biases at the individual level. Using large-scale mobility data, we assess accuracy at individual level of a universal model, the Exploration and Preferential Return (EPR) model, by examining deviations from expected behavior in two scaling laws —one related to exploration and the other to return patterns. Our findings reveal that, while the model can accurately describe population-wide movement patterns, it displays widespread deviations linked to behavioral traits, socioeconomic and lifestyles of individuals, which are not fully captured by the model’s assumptions. In particular, individuals poorly represented by the EPR model exhibit bursty exploration and sequential routine behaviors, associated with stronger temporal irregularity and habitual repetition. Among socioeconomic factors, income emerges as the strongest predictor of deviations from the model, leading to significant spatial disparities in accuracy. Notably, the universal accuracy of the EPR model deteriorates in urbanized and densely populated areas, underscoring essential policy implications. Our results show that reliance on universal mobility models may lead to uneven predictive performance across socioeconomic groups, particularly for vulnerable populations whose mobility patterns diverge most from model assumptions.
NetSI authors
