Multi-attribute reduction of GPS data trajectories: a new approach
Vladimir Usyukov · Procedia Computer Science · 2020
In this paper, we investigate the multi-attribute compression of GPS paths. Most of the research in this area was done to reduce spatiotemporal details of travel paths that draw on the object’s position in a time-space domain. Although these attributes are essential, the significance of other characteristics, such as instantaneous speed and altitude cannot be ignored, due to their importance to location-based applications. We proposed a new approach to tackle this task. Our approach uses an adaptation of the dead-reckoning algorithm for compressing a spatial component, and equal interval classification for reducing instantaneous speed and altitude attribute values.