Journal of Geo-information Science >
Advancements in Semantic Trajectory Modelling and Mining
Received date: 2019-05-27
Request revised date: 2019-10-31
Online published: 2020-06-10
Supported by
National Key Research and Development Program of China(2017YFB0503500)
National Natural Science Foundation of China(41971343)
National Natural Science Foundation of China(41571379)
Copyright
A semantic trajectory is a combination of a spatiotemporal trajectory and semantic information. Besides spatiotemporal information, a semantic trajectory comprises movement states (e.g. speed, direction), external contextual information (e.g. temperature, spatial topological relationships), and social relationships (e.g. friend relationships, social activities) of moving objects. We can derive from semantic trajectories intentions, habits, emotions, and other high order semantic information, thus further discover the patterns, relationships, and rules of individual or collective mobility behaviors. Therefore, compared with spatiotemporal trajectories, semantic trajectories are more in line with the practical requirements of decision-making applications in terms of semantics, interpretation, feasibility, and so on. This paper reviews the key technologies of semantic trajectory mining. First, we introduce the concept of semantic trajectories, and summarize four classic types of semantic trajectory definitions according to semantic elements. Then, we introduce the main phases of semantic trajectory modeling, including preprocessing, trajectory segmentation, and semantic enrichment. Since semantic trajectories cannot be acquired from location-acquisition devices as spatiotemporal trajectories, semantic trajectories need to be obtained through modeling techniques. Thus, the basic idea is to combine spatiotemporal trajectories with semantic information to generate corresponding semantic trajectories. Next, we introduce the main tasks of semantic trajectory mining, including semantic trajectory pattern mining, semantic trajectory clustering, semantic trajectory classification, anomaly detection of semantic trajectories, and so on. For each mining task, this paper introduces the basic principles and related algorithms, and summarizes the main key technologies and challenges. Finally, this paper concludes with the existing challenges and promising research directions of semantic trajectory mining. Specifically, this paper discusses the important research issues of semantic trajectory modeling in aspects including modeling definition, semantic annotation technologies, and multi-source data modeling. This paper also discusses the promising research issues of semantic trajectory mining such as semantic trajectory data management, classification and prediction, trajectory stream mining, privacy protection, multi-granularity mining, and evaluation methods.
ZHAO Bin , HAN Jingjing , SHI Tantan , JI Genlin , LIU Xintao , YU Zhaoyuan . Advancements in Semantic Trajectory Modelling and Mining[J]. Journal of Geo-information Science, 2020 , 22(4) : 842 -856 . DOI: 10.12082/dqxxkx.2020.190257
[1] |
许佳捷, 郑凯, 池明旻 , 等. 轨迹大数据:数据、应用与技术现状[J]. 通信学报, 2015,36(12):97-105.
[
|
[2] |
吉根林, 孙鸿艳, 赵斌 . 时空轨迹群体运动模式挖掘研究进展[J]. 南京航空航天大学学报, 2016,48(5):615-624.
[
|
[3] |
龚玺, 裴韬, 孙嘉 , 等. 时空轨迹聚类方法研究进展[J]. 地理科学进展, 2011,30(5):522-534.
[
|
[4] |
牟乃夏, 徐玉静, 张恒才 , 等. 移动轨迹聚类方法研究综述[J]. 测绘通报, 2018,1(1):1-7.
[
|
[5] |
赵竹珺, 吉根林 . 时空轨迹分类研究进展[J]. 地球信息科学学报, 2017,19(3):289-297.
[
|
[6] |
毛嘉莉, 金澈清, 章志刚 , 等. 轨迹大数据异常检测:研究进展及系统框架[J]. 软件学报, 2017,28(1):17-34.
[
|
[7] |
齐凌艳, 陈荣国, 温馨 . 基于语义轨迹停留点的位置服务匹配与应用研究[J]. 地球信息科学学报, 2014,16(5):720-726.
[
|
[8] |
|
[9] |
|
[10] |
|
[11] |
|
[12] |
吉根林, 赵斌 . 时空轨迹大数据模式挖掘研究进展[J]. 数据采集与处理, 2015,30(1):47-58.
[
|
[13] |
|
[14] |
|
[15] |
|
[16] |
|
[17] |
|
[18] |
|
[19] |
|
[20] |
|
[21] |
|
[22] |
|
[23] |
|
[24] |
|
[25] |
|
[26] |
|
[27] |
|
[28] |
|
[29] |
|
[30] |
|
[31] |
|
[32] |
|
[33] |
|
[34] |
|
[35] |
|
[36] |
|
[37] |
|
[38] |
|
[39] |
|
[40] |
|
[41] |
|
[42] |
|
[43] |
|
[44] |
|
[45] |
|
[46] |
|
[47] |
|
[48] |
|
[49] |
|
[50] |
|
[51] |
|
[52] |
|
[53] |
|
[54] |
|
[55] |
|
[56] |
|
[57] |
|
[58] |
吴瑕, 唐祖锴, 祝园园 . 近似到达时间约束下的语义轨迹频繁模式挖掘[J]. 软件学报, 2018,29(10):3184-3204.
[
|
[59] |
|
[60] |
|
[61] |
高强, 张凤荔, 王瑞锦 , 等. 轨迹大数据:数据处理关键技术研究综述[J]. 软件学报, 2017,28(4):959-992.
[
|
[62] |
|
[63] |
|
[64] |
|
[65] |
|
[66] |
|
[67] |
|
[68] |
|
[69] |
郭黎敏, 高需, 武斌 , 等. 基于停留时间的语义行为模式挖掘[J]. 计算机研究与发展, 2017,54(1):111-122.
[
|
[70] |
|
[71] |
|
[72] |
|
[73] |
|
[74] |
|
[75] |
|
[76] |
|
[77] |
|
[78] |
|
[79] |
|
[80] |
|
[81] |
|
[82] |
|
/
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|
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