• 学术研究 •

### 多角度语义轨迹相似度计算模型

1. 东北大学 计算机科学与工程学院，沈阳 110169
• 出版日期:2021-09-01 发布日期:2021-09-06

### Multi-aspect Semantic Trajectory Similarity Computation Model

CAI Mingxin, SUN Jing, WANG Bin

1. College of Computer Science and Engineering, Northeastern University, Shenyang 110169, China
• Online:2021-09-01 Published:2021-09-06

Abstract:

The development of mobile devices enables trajectory data to record more useful information, such as check in information and activity information, constituting semantic trajectory data. Fast and effective trajectory similarity computation will bring great benefits to the analysis of problems. Scholars have studied trajectory similarity and semantic trajectory similarity and have proposed some effective methods. However, existing trajectory similarity computation methods cannot be applied to semantic trajectory data, and the current semantic trajectory similarity computation methods do not work well under the condition of low trajectory sampling frequency. In this paper, on the basis of solving the sensitivity of trajectory similarity computation to low sampling frequency, combined with the additional visited point information of semantic trajectory, a new trajectory similarity computation model is proposed, which is called multi-aspect semantic trajectory (MAST). The model is based on LSTM (long short-term memory) and introduces the self-attention mechanism. The learned trajectory is expressed as multiple low-dimensional vectors of different aspects of the trajectory, forming a matrix, thereby solving the problem that a single vector cannot accurately express the trajectory. This matrix contains not only the spatial information of the trajectory, but also the semantic information, which can be used to calculate the similarity of the semantic trajectory. MAST is tested on two realistic semantic trajectory datasets. Experimental data show that MAST is superior to existing methods.