  As a significant factor in urban planning, traffic forecasting and prediction
of epidemics, modeling patterns of human mobility draws intensive attention
from researchers for decades. Power-law distribution and its variations are
observed from quite a few real-world human mobility datasets such as the
movements of banking notes, trackings of cell phone users' locations and
trajectories of vehicles. In this paper, we build models for 20 million
trajectories with fine granularity collected from more than 10 thousand taxis
in Beijing. In contrast to most models observed in human mobility data, the
taxis' traveling displacements in urban areas tend to follow an exponential
distribution instead of a power-law. Similarly, the elapsed time can also be
well approximated by an exponential distribution. Worth mentioning, analysis of
the interevent time indicates the bursty nature of human mobility, similar to
many other human activities.
