  Nowadays, various sensors are collecting, storing and transmitting tremendous
trajectory data, and it is known that raw trajectory data seriously wastes the
storage, network band and computing resource. Line simplification (LS)
algorithms are an effective approach to attacking this issue by compressing
data points in a trajectory to a set of continuous line segments, and are
commonly used in practice. However, existing LS algorithms are not sufficient
for the needs of sensors in mobile devices. In this study, we first develop a
one-pass error bounded trajectory simplification algorithm (OPERB), which scans
each data point in a trajectory once and only once. We then propose an
aggressive one-pass error bounded trajectory simplification algorithm
(OPERB-A), which allows interpolating new data points into a trajectory under
certain conditions. Finally, we experimentally verify that our approaches
(OPERB and OPERB-A) are both efficient and effective, using four real-life
trajectory datasets.
