A vehicle behavior similarity calculation method based on bayonet data

Abstract

The invention provides a vehicle behavior similarity calculation method based on bayonet data, Firstly, the data information of the moving object passing through each bayonet is collected and a database is established, and then bayonet data of vehicle A and B is retrieved to form the original trajectory of the two vehicle, the original trajectory is cleaned and cut to obtain a set about sub- trajectories of vehicle A and B, and then the sub-trajectories in the two sets are compared in pairs according to the LCSS algorithm, by calculating the longest common sub-sequence between any two sub-trajectories and the time difference between two vehicles passing through the adjacent bayonets in the longest common sub-sequence, the similarity between the two sub-trajectories is obtained. Finally, the multi-trajectory similarity is calculated by using all the similarity data, and the result is regarded as the behavioral similarity between the two workshops. The invention introduces a time influence factor in the calculation, the obtained result is more accurate and has wider application range, and the trajectory similarity result is also used for judging the similarity degree of the vehicle behavior.

Description

technical field [0001] The invention relates to the technical field of data processing and analysis of moving objects, in particular to a method for calculating vehicle behavior similarity based on bayonet data. Background technique [0002] In recent years, due to the continuous development and improvement of big data monitoring systems and the increasing demand for practical applications, the analysis of moving object trajectory data has attracted more and more attention. Trajectory data is essentially a large number of trajectory points, and each trajectory point includes at least the following information: object, position and time; if all the spatial positions of the same object in continuous time are collected and marked, a space-time trajectory can be obtained. [0003] At present, major cities in China have established relatively sound information infrastructure, providing massive data resources and information platforms for the collection and utilization of trajecto...

AI Technical Summary

Problems solved by technology

Since the data collection points are all set at the checkpoints, the location is special, so the longest common subsequence algorithm is usually used, but because the algorithm only introduces the space tolerance (that is, the position distance between the checkpoints) without considering the time tolerance , so there is a defect: for different trajectories that pass through the same bayonet but with a large difference in time, the calculated multiple similarities may be highly similar, so it is difficult to play a practical role in the analysis results

Based on the above defects, the existing trajectory similarity calculation method has poor reliability and a small scope of application, let alone further extending and applying it to the analysis and judgment of the similarity of different vehicle behaviors

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