A Constrained Trajectory Line Simplification Method Preserving Speed ​​Characteristics

Abstract

The invention discloses a simplification method for a road network constraint track line keeping speed characteristics. Firstly, the road network is organized as a geographically complete road entity, and the trajectory point matching is shifted to the road arc segment based on the principle of the closest distance. And the simplification model is designed, which are (1) geometric simplification operator, which implements geometric simplification on the road network constraint trajectory based on the network space distance, and ensures that the maximum position error of the trajectory point after simplification does not exceed the set threshold ε( ε≥0); (2) Velocity enhancement operator, which enhances the local velocity information of the simplified trajectory, so that the maximum error of the implied velocity of the trajectory before and after the simplification does not exceed the set threshold μ (μ≥0). The present invention introduces network space distance to construct a road constraint trajectory line simplification method that conforms to the position error boundary, and enhances the speed information of the simplification result in the form of an additional array, so that the constraint trajectory line can keep the original geometric form as much as possible while implementing geometric form simplification. movement speed characteristics.

Description

technical field [0001] The invention belongs to the technical field of geographic information, and relates to a method for simplifying spatio-temporal trajectory data, in particular to a method for simplifying a road network constraint trajectory line while maintaining speed characteristics. Background technique [0002] By tracking moving objects such as vehicles, animals, ships, and pedestrians, the spatio-temporal trajectory data composed of time-stamped position points can be obtained. With the continuous advancement of mobile positioning technology, the trajectory tracking of moving targets is more accurate and the economic cost is lower. Against this backdrop, recording trajectories of various moving objects is becoming ubiquitous and growing rapidly. These trajectory data provide rich information for traffic monitoring and visualization, animal behavior analysis, urban computing, and travel itinerary recommendation. However, the trajectory data is usually collected ...

AI Technical Summary

Problems solved by technology

However, when applied to the simplification of constrained trajectories (such as vehicle trajectories in road network space), there are the following deficiencies: (1) Existing simplification methods mainly use Euclidean distance instead of network distance, resulting in the The position error evaluation is not accurate enough; (2) The existing methods mainly focus on maintaining the geometric accuracy of the trajectory before and after simplification, but lack effective consideration of the moving speed, which may lead to inaccurate spatio-temporal feature analysis in the later stage

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