Research of trajectory similarity metric based on improved graph edit distance and ITS application

In order to better analyze and process National Day highway travel trajectory data, a method and its calculation method for measuring the similarity between trajectories were proposed. Firstly, a new method for measuring trajectory similarity, namely TGI_GED, was introduced in this paper. Secondly, a graph neural network model was employed to enhance the computational efficiency of TGI_GED, enabling the calculation of TGI_GED between complex trajectories. Finally, this method combined clustering algorithms to further explore the characteristics and patterns of trajectories. Experimental results indicated that the graph neural network model not only significantly improved the computational efficiency of TGI_GED but also allowed for the calculation of TGI_GED between complex trajectories. Additionally, clustering results suggested that TGI_GED was more suitable for measuring the similarity between vehicle trajectories than graph edit distance. TGI_GED facilitated the discovery of latent patterns in trajectory data, offering a fresh perspective and solution for research in the field of vehicle trajectories.