With the development of various navigation and positioning technolo-gies and the widespread use of global positioning system(GPS)devices,the scale of trajectory data generated has significantly increased.Simultaneously...With the development of various navigation and positioning technolo-gies 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 com-pression methods,whether based on location information or directional informa-tion,only retain the longitude and latitude information of key position points in the trajectory and are unable to restore all trajectory points for analysis.Addition-ally,when optimizing,current trajectory compression methods primarily utilize road network information for vehicles and pedestrians;however,few have con-sidered the spatial-temporal characteristics of the trajectory itself.For example,in specific scenarios such asflights,trajectories often exhibit numerous straight-line segments,offering opportunities for further compression improvements.Based on this observation,this paper analyzes the data characteristics offlight trajec-tories and proposes a bounded-error trajectory compression algorithm based on the deviation angle and relative distance.To the best of our knowledge,this is thefirst article that records trajectory points in the form of<angle,distance>tuple instead of traditional<latitude,longitude>methods.Experiments conducted on real-life datasets under various setting conditions are compared with those of clas-sic 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.In our algorithm,each point beyond thefirst two is defined by its distance to the preceding point,di,i-1,allowing for trajectory reconstruction.The deviation angle,φ,is determined by comparing the accumulated deviation,α,to a set threshold,θ.Whenα>θ,this deviation is noted at the trajectory point,indicating a navigational adjustment,a key spatiotemporal marker.Points not surpassing this threshold do not have their angle data recorded,shifting from latitude and longitude to a format emphasizing distance and significant cumulative angle changes.This approach,as detailed in Sect.3,leverages the fact that linear trajectory segments constitute more than 95%of the data,enabling substantial angle information reduction for compression purposes.展开更多
基金supported by the National Natural Science Foundation of China under grant No.62102434 and No.U22B2005.
文摘With the development of various navigation and positioning technolo-gies 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 com-pression methods,whether based on location information or directional informa-tion,only retain the longitude and latitude information of key position points in the trajectory and are unable to restore all trajectory points for analysis.Addition-ally,when optimizing,current trajectory compression methods primarily utilize road network information for vehicles and pedestrians;however,few have con-sidered the spatial-temporal characteristics of the trajectory itself.For example,in specific scenarios such asflights,trajectories often exhibit numerous straight-line segments,offering opportunities for further compression improvements.Based on this observation,this paper analyzes the data characteristics offlight trajec-tories and proposes a bounded-error trajectory compression algorithm based on the deviation angle and relative distance.To the best of our knowledge,this is thefirst article that records trajectory points in the form of<angle,distance>tuple instead of traditional<latitude,longitude>methods.Experiments conducted on real-life datasets under various setting conditions are compared with those of clas-sic 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.In our algorithm,each point beyond thefirst two is defined by its distance to the preceding point,di,i-1,allowing for trajectory reconstruction.The deviation angle,φ,is determined by comparing the accumulated deviation,α,to a set threshold,θ.Whenα>θ,this deviation is noted at the trajectory point,indicating a navigational adjustment,a key spatiotemporal marker.Points not surpassing this threshold do not have their angle data recorded,shifting from latitude and longitude to a format emphasizing distance and significant cumulative angle changes.This approach,as detailed in Sect.3,leverages the fact that linear trajectory segments constitute more than 95%of the data,enabling substantial angle information reduction for compression purposes.
