Track similarity connection query method adopting two-stage search

Abstract

The invention discloses a track similarity connection query method adopting a two-stage search. The method is used for searching for track pairs, with time-space similarity greater than a similarity threshold, between two track sets P and Q according to the given two track sets P and Q and similarity threshold. The method comprises the following steps of 1) for each track in the set P, searching for a track similar to the track in time and space dimensions in the set Q and storing the track as a candidate set of the track; and 2) combining the candidate sets of all the tracks in the set P to obtain all the track pairs meeting the requirements. The method can be applied to a road network related scene; the similarity between the tracks is calculated by using a continuity matching method intime and space dimensions; the matching degree between the two tracks is better measured; and a user-satisfied result is output.

Description

technical field [0001] The invention relates to a trajectory similarity query method, in particular to a trajectory similarity connection query method using two-stage search. Background technique [0002] With the rapid development of GPS positioning technology and online map services, the collection and sharing of trajectory data has become more and more common. As a result, various new methods of trajectory data management and analysis have been born. At present, most of the existing trajectory similarity queries are based on time periods, which are mainly divided into two categories: (1) Given a matching threshold in the time dimension, if the gap between two corresponding trajectory points exceeds this threshold, It is considered that there is no match, and if it is less than, it is considered to be a match. (2) Given a time window for trajectory matching, match the trajectories within the window, and mainly consider the spatial similarity when matching. However, in p...

AI Technical Summary

Problems solved by technology

However, in practical applications, only considering the spatial similarity is not enough to measure the matching degree between two trajectories

For example, in the itinerary recommendation scenario, the system may recommend two people with similar itineraries but very different departure times as carpool travel objects. Although their travel trajectories are very similar in spatial dimensions, it is obvious that users will not be satisfied with such a result. satisfy

[0003] In addition, the existing technology still has the following shortcomings: (1) It is mainly based on the Euclidean space, not the road network. The existing spatial index structure is not suitable for the trajectory similarity query based on the road network, and cannot give accurate search results , because moving objects (pedestrians, vehicles) mainly move in the road network, not in the Euclidean space

(2) Different time matching methods. Existing methods are based on time period matching. (3) Parallel processing is not supported. Existing methods are stand-alone versions, so real big data cannot be processed.

None of the existing methods can solve the trajectory similarity join query in road network

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