One-pass trajectory simplification using the synchronous Euclidean distance

Abstract

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 bandwidth and computing resource. To attack this issue, one-pass line simplification ($$\textsf {LS} $$) algorithms have 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 ($$\textsf {SED} $$), and cannot support spatiotemporal queries. To do this, we develop two one-pass error bounded trajectory simplification algorithms ($$\textsf {CISED} $$-$$\textsf {S} $$ and $$\textsf {CISED} $$-$$\textsf {W} $$) using $$\textsf {SED} $$, based on a novel spatiotemporal 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 $$\textsf {CISED} $$-$$\textsf {S} $$ and $$\textsf {CISED} $$-$$\textsf {W} $$ are on average 3 times faster than $$\textsf {SQUISH} $$-$$\textsf {E} $$ (the fastest existing $$\textsf {LS} $$ algorithm using $$\textsf {SED} $$). In terms of compression ratios, $$\textsf {CISED} $$-$$\textsf {S} $$ is close to and $$\textsf {CISED} $$-$$\textsf {W} $$ is on average $$19.6\%$$ better than $$\textsf {DPSED} $$ (the existing sub-optimal $$\textsf {LS} $$ algorithm using $$\textsf {SED} $$ and having the best compression ratios), and they are $$21.1\%$$ and $$42.4\%$$ better than $$\textsf {SQUISH} $$-$$\textsf {E} $$ on average, respectively.