  Various mobile devices have been used to collect, store and transmit
tremendous trajectory data, and it is known that raw trajectory data seriously
wastes the storage, network band and computing resource. To attack this issue,
one-pass line simplification (LS) algorithms have are been developed, by
compressing data points in a trajectory to a set of continuous line segments.
However, these algorithms adopt the perpendicular Euclidean distance, and none
of them uses the synchronous Euclidean distance (SED), and cannot support
spatio-temporal queries. To do this, we develop two one-pass error bounded
trajectory simplification algorithms (CISED-S and CISED-W) using SED, based on
a novel spatio-temporal cone intersection technique. Using four real-life
trajectory datasets, we experimentally show that our approaches are both
efficient and effective. In terms of running time, algorithms CISED-S and
CISED-W are on average 3 times faster than SQUISH-E (the most efficient
existing LS algorithm using SED). In terms of compression ratios, algorithms
CISED-S and CISED-W are comparable with and 19.6% better than DPSED (the most
effective existing LS algorithm using SED) on average, respectively, and are
21.1% and 42.4% better than SQUISH-E on average, respectively.
