Understanding and Visualisation of Geographic Mesh Similarity by Trajectory Data and Gaussian Process Modelling
Understanding and Visualisation of Geographic Mesh Similarity by Trajectory Data and Gaussian Process Modelling
复制标题
通过轨迹数据和高斯过程建模理解和可视化地理网格相似性
DOI：
10.1007/s13177-018-0171-9
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发表时间：
2018
影响因子：
2.1
通讯作者：
Nakanishi Wataru
中科院分区：
文献类型：
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作者：
Nakanishi Wataru
提出了一种基于轨迹数据的网格相似性估计的新概念。该模型被制定为一个无监督的学习方法，使用一种类型的高斯过程的连续坐标系。这允许将网格和轨迹的特征确定为不同于地理坐标的估计的潜在坐标。网格和轨迹的相似性通过坐标的相似性来表示。此外，这允许容易的可视化。在介绍了一种马尔可夫链蒙特卡罗方法的坐标估计方法后，使用日本仙台市的实际轨迹数据对所提出的方法进行了验证。
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.