Track compression method based on moving behavior characteristics

Abstract

The invention provides a track compression method based on moving behavior characteristics. The track compression method comprises the following steps: acquiring an acquisition point of a track; if the current acquisition point is a starting point, regarding the current acquisition point as a feature point and stored, and meanwhile, assigning the previous feature point as the value of the current acquisition point; otherwise, calculating the azimuth angle variation of the current acquisition point relative to the previous acquisition point and the previous characteristic point, and calculating the speed change rate of the current acquisition point; if the larger value of the azimuth angle variable quantity and the speed change rate are both smaller than the corresponding preset threshold values, processing is not carried out; otherwise, regarding the characteristic points as characteristic points and storing the characteristic points, and meanwhile, assigning the previous characteristic point as the value of the current acquisition point; and finally, assigning the previous acquisition point as the value of the current acquisition point, and continuing to execute the above steps on the next acquisition point of the current acquisition point. Compared with the prior art, the method has the advantages that the characteristic points are captured more accurately, and the driving state information under the situations of rapid acceleration, rapid deceleration, rapid steering and the like can be stored.

Description

technical field [0001] The invention relates to the technical field of trajectory compression, in particular to a trajectory compression method based on moving behavior characteristics. Background technique [0002] The development of Internet of Things technology and the popularization of mobile terminals have produced massive mobile behavior characteristic data. According to the data of mobile behavior characteristics, the whereabouts of users can be monitored, and the driving behavior of users can be evaluated and intervened to improve the level of safe driving of vehicles. Massive mobile behavior feature data not only increases the cost of storage and transmission, but also increases the processing complexity of applications such as data mining and trajectory monitoring. Therefore, it is necessary to compress massive data and provide high-quality data for applications such as data mining and trajectory monitoring. [0003] Existing trajectory compression methods mainly...

AI Technical Summary

Problems solved by technology

The distance-based line segment simplification method is to retain important feature points that meet a certain distance condition; the road network matching method is to combine the positioning data with the digital map, and map the positioning data to the corresponding road network position. However, this method is based on the "trajectory Always drive along the shortest path of the road network" assumption, the actual running trajectory is often unable to meet the assumption due to the influence of complex external environment

Due to the reason of the method itself, the above two trajectory compression methods pay more attention to the compression rate and compression accuracy, and it is easy to lose the feature points with changes in speed and direction, so that the unstable driving behavior feature points cannot be detected.

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