| 摘要: |
| 基于地理位置信息的应用和服务的迅速发展，对轨迹数据挖掘提出了新的需求和挑战.原始轨迹数据通常是由坐标-时间戳元组构成的有序序列，而现有的大多数数据分析算法均要求输入数据位于向量空间中.因此，为了将轨迹数据从变长的坐标-时间戳序列转化成定长的向量表示且保持原有的特征，对轨迹数据进行有效的表示是十分重要且必要的一步.传统的轨迹表示方法大多是基于人工设计特征，通常仅将轨迹表示作为数据预处理的一部分.随着深度学习的兴起，这种从大规模数据中学习的能力使得基于深度学习的轨迹表示方法相比于传统方法取得了巨大的效果提升，并赋予了轨迹表示更多的可能性.对轨迹表示领域中的研究进展进行了全面的总结，将轨迹表示按照研究对象的不同尺度，归纳为对轨迹单元的表示和对整条轨迹的表示两大类别，并在每种类别下对不同原理的方法进行了对比分析.其中重点分析了基于轨迹点的表示方法，也对近年来广泛使用的基于神经网络的轨迹表示的研究成果做了系统的归类.此外，介绍了基于轨迹表示的关键应用，最后对轨迹表示领域的未来研究方向进行了展望. |
| 关键词: 轨迹数据挖掘 轨迹表示 时空数据挖掘 |
| DOI：10.13328/j.cnki.jos.006210 |
| 分类号:TP181 |
| 基金项目:国家自然科学基金（52071312）；之江实验室开放课题（2019KE0AB03） |
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| Survey on Trajectory Representation Learning Techniques |
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CAO Han-Lin1, TANG Hai-Na1, WANG Fei2, XU Yong-Jun2
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1.School of Artificial Intelligence, University of Chinese Academy of Science, Beijing 100049, China;2.Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
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| Abstract: |
| The rapid development of location-aware applications and services poses new challenges for trajectory data mining. The raw trajectory data usually consist of ordered sequences of coordinate-timestamp tuple, while many algorithms widely used for data analysis require input data to be in vector space. Therefore, it is an important and necessary step to effectively represent trajectory data from variable-length coordinate-timestamp sequence to a fixed-length vector that maintains the spatial-temporal characteristics of the movement. Most conventional trajectory representation methods are based on feature engineering, in which trajectory representation is usually considered as part of the data preprocessing. With the prevalence of deep learning, the ability of learning from large-scale data endows deep learning-based methods for trajectory representation with more potential and vitality, which achieved better performance compared to traditional methods. This paper provides a comprehensive review of recent progress in trajectory representation and summarizes the trajectory representation methods into two categories according to the different scales:trajectory unit representation and entire trajectory representation. In each category, the methods of different principles are compared and analyzed. Among them, the methods based on trajectory point are emphasized, and also the widely used methods based on neural networks are systematically classified. Besides, the applications related to trajectory representation under each category are introduced. Finally, the future research directions are pointed out in the field of trajectory representation. |
| Key words: trajectory data mining trajectory representation spatial-temporal data mining |
