This study investigates human mobility patterns using data from 100,000 anonymized mobile phone users tracked over six months. The results show that human trajectories exhibit high temporal and spatial regularity, with individuals characterized by a time-independent characteristic length scale and a significant probability of returning to a few frequently visited locations. Despite the diversity of travel histories, individual travel patterns collapse into a single spatial probability distribution, indicating simple, reproducible patterns. Human mobility is often modeled using random walk or diffusion models, but the study finds that human trajectories are better modeled as a continuous time random walk with fat-tailed displacements and waiting time distributions. The observed distribution of travel distances is well approximated by a truncated power-law, with a scaling exponent close to that observed in bank note dispersal, suggesting a common mechanism. The study distinguishes between three hypotheses for the observed distribution: individual Lévy trajectories, population-based heterogeneity, or a combination of both. Analysis of the radius of gyration (typical distance traveled) shows that the distribution of this quantity follows a truncated power-law, and that the time dependence of the radius of gyration is better approximated by a logarithmic increase, not a power-law. The study also finds that individuals tend to return to a few highly frequented locations, and that the probability of finding a user at a location with a given rank is well approximated by $ P(L) \sim 1/L $, indicating that people spend most of their time at a few locations. The study further shows that the shape of human trajectories is spatially anisotropic, with the degree of anisotropy increasing with the radius of gyration. After rescaling the data, the probability distribution of finding a user in a location is found to be universal across individuals, suggesting that key statistical characteristics of individual trajectories are largely indistinguishable after rescaling. These findings suggest that human mobility patterns can be modeled using simple, reproducible patterns, and that the observed Lévy statistics in bank note measurements may reflect a convolution of population heterogeneity and individual mobility. The results have implications for modeling human mobility in various contexts, including epidemic prevention, urban planning, and agent-based modeling.This study investigates human mobility patterns using data from 100,000 anonymized mobile phone users tracked over six months. The results show that human trajectories exhibit high temporal and spatial regularity, with individuals characterized by a time-independent characteristic length scale and a significant probability of returning to a few frequently visited locations. Despite the diversity of travel histories, individual travel patterns collapse into a single spatial probability distribution, indicating simple, reproducible patterns. Human mobility is often modeled using random walk or diffusion models, but the study finds that human trajectories are better modeled as a continuous time random walk with fat-tailed displacements and waiting time distributions. The observed distribution of travel distances is well approximated by a truncated power-law, with a scaling exponent close to that observed in bank note dispersal, suggesting a common mechanism. The study distinguishes between three hypotheses for the observed distribution: individual Lévy trajectories, population-based heterogeneity, or a combination of both. Analysis of the radius of gyration (typical distance traveled) shows that the distribution of this quantity follows a truncated power-law, and that the time dependence of the radius of gyration is better approximated by a logarithmic increase, not a power-law. The study also finds that individuals tend to return to a few highly frequented locations, and that the probability of finding a user at a location with a given rank is well approximated by $ P(L) \sim 1/L $, indicating that people spend most of their time at a few locations. The study further shows that the shape of human trajectories is spatially anisotropic, with the degree of anisotropy increasing with the radius of gyration. After rescaling the data, the probability distribution of finding a user in a location is found to be universal across individuals, suggesting that key statistical characteristics of individual trajectories are largely indistinguishable after rescaling. These findings suggest that human mobility patterns can be modeled using simple, reproducible patterns, and that the observed Lévy statistics in bank note measurements may reflect a convolution of population heterogeneity and individual mobility. The results have implications for modeling human mobility in various contexts, including epidemic prevention, urban planning, and agent-based modeling.