Leveraging Spatial Characteristics in Trajectory Compression: An Angle-Based Bounded-Error Method

With the development of various navigation and positioning technologies and the widespread use of global positioning system (GPS) devices, the scale of trajectory data generated has significantly increased. Simultaneously, the demand for real-time data processing has gradually risen. Current trajectory compression methods, whether based on location information or directional information, only retain the longitude and latitude information of key position points in the trajectory and are unable to restore all trajectory points for analysis. Additionally, when optimizing, current trajectory compression methods primarily utilize road network information for vehicles and pedestrians; however, few have considered the spatial-temporal characteristics of the trajectory itself. For example, in specific scenarios such as flights, trajectories often exhibit numerous straight-line segments, offering opportunities for further compression improvements. Based on this observation, this paper analyzes the data characteristics of flight trajectories and proposes a bounded-error trajectory compression algorithm based on the deviation angle and relative distance. To the best of our knowledge, this is the first article that records trajectory points in the form of  tuple instead of traditional  methods. Experiments conducted on real-life datasets under various setting conditions are compared with those of classic compression algorithms. The most significant advantage is that our algorithm exhibits the fastest compression speed, averaging 0.027 s for compressing a single trajectory. When the distance threshold is set to 75 m, the DP algorithm consumes 15 times more time than our algorithm, while the SW algorithm takes even longer. Given the threshold constraints, AB-C also performs well in terms of angular and distance errors.