A new concept is proposed of estimating mesh similarity based on trajectory data. The model is formulated as an unsupervised learning method using a type of Gaussian process on a continuous coordinate system. This allows for the features of meshes and trajectories to be determined as the estimated latent coordinates that are different from geographic ones. The similarities of meshes and trajectories are represented through those of coordinates. In addition, this allows for easy visualisation. After introducing the coordinate estimation method with a type of Markov Chain Monte Carlo approach, the proposed method was verified using actual trajectory data from the city of Sendai, Japan.
Understanding and Visualisation of Geographic Mesh Similarity by Trajectory Data and Gaussian Process Modelling
Int. J. ITS Res.
International Journal of Intelligent Transportation Systems Research ; 18 , 1 ; 35-42
2020-01-01
8 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Mesh similarity , Trajectory analysis , Latent coordinates , Gaussian process , Visualisation Engineering , Electrical Engineering , Automotive Engineering , Robotics and Automation , Computer Imaging, Vision, Pattern Recognition and Graphics , Civil Engineering , User Interfaces and Human Computer Interaction
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