One-pass trajectory simplification using the synchronous Euclidean distance

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 ( 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 ( SED ), and cannot support spatiotemporal 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 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 CISED - S and CISED - W are on average 3 times faster than SQUISH - E (the fastest existing LS algorithm using SED ). In terms of compression ratios, CISED - S is close to and CISED - W is on average 19.6 % better than DPSED (the existing sub-optimal LS algorithm using SED and having the best compression ratios), and they are 21.1 % and 42.4 % better than SQUISH - E on average, respectively.